As enterprises are embarking on the route of digital transformation, DevOps has become more crucial than ever. It has come out as the call for the hour for the current IT firms to fill the gap between the development team and the operations team. Apart from encouraging a collaborative culture, DevOps has fostered rapid and reliable software delivery, improved customer satisfaction, enhanced time-to-market, and much more. Given such services, DevOps practices have become critical for most enterprises and are rising in fame with time. Markets and Markets report says the DevOps market size is likely to climb from $2.90 Billion in 2017 to $ 10.31 Billion by 2023.
With the change in the business scenario and the new-edge technology evolving at high speed, DevOps has also grown to suit the changing needs. Let’s take a glance at some of the 2021 rising DevOps trends expected to reshape the business landscape in the coming future.
Emerging DevOps Trends for 2022 and Beyond
1. Incorporation of Kubernetes with DevOps
The crucial infrastructure trend that affects DevOps is the growing adoption of Kubernetes by global tech companies. It has become an apt choice by organizations to manage software delivery. With Kubernetes, the software developers can easily share various apps and software with the IT operations team in real-time. Productivity gets improved by selecting the Kubernetes workflow, as it offers ease to the build, testing, and deployment pipelines in DevOps.
Container management systems and Kubernetes can also reduce the necessity for human interaction and promote a fully automatic, ‘NoOps’ scenario. Its pipeline architecture approach makes it simpler for developers to use ML and AI tools to examine, predict, and automate records and work processes.
In the industry, various vendors are excited to expand their support to Kubernetes. Leading software providers like RedHat and VMWare have already supported Kubernetes. The most common reason is its competence to reform cloud-based apps through container-centric Microservices. With Kubernetes continuing its grip in the DevOps arena, 2021 could be an excellent year for tools that support it.
2. Massive Rise of Cloud-Native Technology
The cloud-native stack, also called the new stack, is the modern paradigm of cloud-hosted solutions used to build and run apps. The adoption of this latest technology can entail a greater degree of improvement, progression, and innovation. Unlike Cloud managed services, they are configurable, infrastructure-independent, and, in some cases, extra secure.
Cloud-native typically refers to a container-based system. This innovative technology supports the platforms to build apps with services that involve containers. They deployed as a part of microservices, run in containers, and managed using agile and DevOps methodology. The ultimate aim of cloud-native is to enhance the service assembly’s speed and efficiency, allowing the business to react rapidly to market change. Most IT firms are opting for this technology based on various methodologies like Microservices.
With the phenomenal success of Netflix who leveraged cloud technologies, several business platforms are shifting their services/ solutions to Cloud-native technology.
Artificial Intelligence and Machine Learning have left nothing untouched in the technical arena. DevOps isn’t left behind — this technological procedure has taken in much of AI and ML methods to find the best results. AI can change how teams develop, deliver, deploy and organize apps to boost their performances without much effort.
Application of Artificial Intelligence and Machine Learning results in real return for a business, making IT operations extra responsive. They can improve the team’s productivity and play an influential role in filling the gap between humans and big data. AI has now become a valuable asset to assist in the decision-making process in DevOps. The newest terminologies like AIOps, DataOps, are also coming into reality, and their importance will grow over the next year.
4. High Demand of Infrastructure as Code (IaC)
IaC (Infrastructure as code) is more than just automation and has become crucial in DevOps. It is more like managing the complete IT infrastructure in the Cloud through configuration files. Most importantly, it ensures continuity, as all the environments are automatically provisioned and configured with zero human error.
Some crucial benefits of IaC comprise easier cloud-native adoption, increasingly ephemeral architecture, traceability, constancy in deploying similar configurations, and higher efficiency during the full software development cycle. Even reversing the procedure to the “last configuration that worked” is possible with Infrastructure as code. With more and more teams realizing these benefits, IaC will continue to be the standard and prominent DevOps aspect in 2021 and the near future.
5. GitOps Growing Adoption
GitOps- a new entrant to the DevOps workflow provides a way to automate and control infrastructure. “One of the critical functions of GitOps is to enable a group of system changes to be applied correctly and then verified,” said Alexis Richardson, the CEO and founder of Weavework.”
It uses similar DevOps best practices that several teams use, like code review, version control, and CI/CD pipelines. With GitOps, teams can effortlessly automate the infrastructure provisioning procedure. The declaration files can be easily stored in a Git repository, just as we store app development code. Thus, it reduces downtime making the deployments reliable and faster. Teams that implement GitOps can use a similar paradigm for server infrastructure, apps, and even Kubernetes clusters.
Given the complexity of multi-cloud, hybrids, and edge app deployments, leading players like MS Azure and Amazon AWS, among others, have expressed support for this paradigm.
6. The Upsurge in Adoption of DevSecOps
Security will remain the main concern for companies of all sizes, so it is no surprise there will be an upsurge in DevSecOps adoption than ever during 2021. DevSecOps adds robust security means to traditional DevOps practices from day 1. The incorporation of Development + Security + Operations, called DevSecOps, guarantees tight cybersecurity protocols in each development life cycle layer.
DevSecOps offerings can easily incorporate with standard CI/CD tests tools. As a result, enterprises can notice significant cybersecurity improvements and overall IT effectiveness. Through the DevSecOps-centric approach, developers can ensure that security is injected into every development life cycle layer, enabling any threats to be detected and mitigated rapidly. In a nutshell, security will no longer be a second thought in DevOps pipelines.
What does Stats say?
According to the Markets and Markets statement on the DevSecOps Global Forecast, the DevSecOps market size is likely to boost from $ 1.5 billion in 2018 to $ 5.9 billion by 2023.
Overall, the DevOps adoption is truly a valuable investment for your upcoming business growth. A recent study says that leading organizations that have incorporated DevOps practices in their development life cycle have gained a 63% boost in the software deployments quality.
Embrace these newest trends on time and allow your business to remain competitive in the Tech world.
AI (Artificial Intelligence) is the cutting-edge technology to be leveraged amid all domains and verticals. On a similar note, Artificial Intelligence is being leveraged in the software testing and QA field to ease the test procedure and deliver higher quality outcomes. For the successful introduction of the newest software, it is inevitable to perform tests in a smooth way. It is quite obvious that the key to making QA tests effective lies with AI (Artificial Intelligence). By using technology, which can thoroughly work like humans, QA specialists can go beyond the old method of manual tests and progress towards a precision-based tests system.
Interestingly, the AI (Artificial Intelligence) test framework can distinguish pitfalls more effectively and with constant upgrades to the algorithms, it is feasible to notice even the smallest number of errors. AI test framework requires small maintenance and can discover new paths on its own. With enterprises waking to AI, testers can use it to simplify the decision-making procedure and enhance effectiveness in the QA field. This point of optimization can hit an unbelievable height with the introduction of Artificial Intelligence. Artificial Intelligence performs recurring mundane work, gives reports on code quality, and helps systematize the process. AI can also assist teams to learn and assist, with ample business apps — the possibilities are unlimited.
Multiple sectors like manufacturing, healthcare, finance, and logistics are leveraging AI to streamline their procedures. Incorporating Artificial Intelligence in the framework hugely encompasses the use of Artificial Intelligence-based apps and IoT devices to control and automate the procedures involved. The success or the failure of these Artificial Intelligence apps will have an effect on the business consequently. Therefore, the testing of these applications becomes crucial before deployment. However, software tests hold series of challenges. These count a lack of awareness, challenges related to stability, performance, functionality, and scalability in real-world apps.
The Significance of Artificial Intelligence in Software Testing
The increase of automated testing has coincided with the acceptance of agile methodologies in software development. This allows the QA specialists group to deliver error-free and robust software in small batches. Manual test is restricted to business acceptance test merely. DevOps test along with Automation helps agile groups to ship a guaranteed product for SaaS/ cloud deployment through a Continuous Integration/ Continuous Delivery pipeline.
In software testing, Artificial Intelligence is a blend of machine learning, cognitive automation, reasoning, analytics, and natural language processing. Cognitive automation leverages several technological approaches such as data mining, semantic technology, text analytics, machine learning, and natural language processing. For instance, Robotic Process Automation (RPA) is one such connecting link between Artificial Intelligence and Cognitive Computing.
The use of Artificial Intelligence in QA can aid overcome these disputes and make the software-testing procedure less-tedious and automated. So, let us see how Artificial Intelligence has changed the traditional method of software tests.
1. Automatically Writing Test Cases
The huge app of Machine Learning/ Artificial Intelligence in the automated tests has been in automatically writing test cases for apps or software. In the early days, we have heard about web crawlers as well as “spidering” (browsing a web/ software in a methodical and automated manner using an automated program or script) which assisted us to find 404 dead pages.
Now, Machine learning/ AI tools have gone far ahead to learn the business use scenarios of the app under testing. They simply require to be pointed to the software. Whilst learning the app, they automatically crawl & gather beneficial data such as HTML pages and page loading time, and screenshots. Over time they gather sufficient data from the app so that they can train the Machine learning model for expected patterns of the application.
When they are executed/ run, the present condition of the application is compared with the known or saved patterns. If there is any visual difference, error, slow run time, or same problem, then the automatic system marks it as a potential problem. But, in few cases, the distinctions might be valid. In that case, the QA expert needs to validate the glitch or bug.
2. Visual Validation Automated Tests
Visual validation test is a part of QA where testing estimates if the User Interface is displayed properly to the end-user. The objective of the testing is not to ensure that the solution delivers the expected performance, however, scrutinize that every UI component appears in the correct size, position, color, and shape.
Automated visual test is difficult as there are innumerable scenarios of possible bugs. Testers need to visualize users’ frame of mind and view the User Interface with their eyes. It’s tough to even for a manual tester, much less for an automated program. This is why changing the visual tests to automated solutions is connected to reluctant specifications and details — QA experts aim to set the condition precisely, yet end up with a messed up file — which is practically not possible to implement.
Artificial Intelligence analyzes the environment in which the app runs — operating systems, browsers, hardware requirements, and detects which User Interface standards are applicable. Unlike regular automated visual validation testing, Artificial Intelligence-based scenarios adapt to consumer’s needs.
3. Enhances Reliability
Are you one of those whose Selenium tests or UFT fail due to small modifications to the app (like resizing or renaming a field) made by the software developers? If yes, then do not be anxious, this is an issue most QA experts face. Now Artificial Intelligence can correct the code and make it extra manageable and reliable so that you don’t have to modify the test every time developers make a small change.
Artificial Intelligence/ Machine Learning tools can read the modification made to the app and understand the relationship between them. Such self-healing test scripts notice alterations in the app and start learning the pattern of modification and then can recognize a change at runtime without you having to perform anything. As the application evolves the Machine Learning scripts automatically adjust, decreasing the fragility and flakiness of automated testing.
4. Reduced User Interface-based Tests
Another modification brought by Machine Learning/ AI to automated tests is automation without UI. Non-functional testing such as performance, Unit Integration, vulnerability, and security are also no exception. Artificial Intelligence/ Machine Learning-based techniques can be applied to generate testing in these layers. Besides, AI/ML applied on several app logs like production monitoring system logs and source code, aids in developing error prediction, self-healing, early notification, and auto-scaling capacities in the general software eco-system.
Artificial Intelligence-based tests reduce overall tests cost, time, scripting, and error. Isn’t it accurately what we desire? There is no suspicion that Machine Learning and AI are game-changers in the QA industry and therefore it will become a trend in the market soon. It’s high time QA teams move towards an Artificial Intelligence-based approach for Software Development, Management, and Testing.
5. Self-Repair engaged in the execution of Selenium Tests
Selenium testing is productive tests frameworks. However at times, they are time-taking, complicated, and even a minor technical error can result in failure of test case growth. The Artificial Intelligence-based solution automatically determines such errors and aids self-repair. It also gives smart technical insights to improve other tests procedures.
6. Speedy time to market
The incorporation of the two above-mentioned benefits assists developers as well as executors. The use of Artificial Intelligence in QA tests can lead to better app development with the shortest periods required for tests. Hence, the end product can be advertised, marketed, and employed for commercial usage at the earliest. Therefore, software developers can build up the product faster with the least amount of bugs, and the users can start using the product at the earliest, and in due course the end-customer. Hence, it is a win-win situation for each party engaged.
7. Prognostic Analysis
AI-based tests can utilize the existing client data & analytics data to find out how users’ demands & users browsing behaviors will develop in the future. It makes certain that the QA developers and testers are one step ahead of the consumer & their demands. With Artificial Intelligence-based solutions, there will be improved service quality and better prediction of growing needs.
8. Reliable and Effective
Artificial Intelligence algorithms have introduced efficiency in the QA tests. The AI theories have also enhanced the reliability of the test methods by lessening the manpower and also the rigorous costs. The procedure is reliable as the glitches will be checked by examining codes that will not leave the flaws unattended without resolving them.
9. Improved Quality
With the employment of artificially encouraging intelligence, the software’s quality will develop extensively. Since all the tests techniques will be performed automatically and with secured certainty, the quality will be enhanced greatly. Furthermore, the app’s longevity will be enhanced greatly along with the improved market effectiveness.
10. Earliest Response/ Feedback
As the Artificial Intelligence-based tests procedure is automated, the developers will get a rapid feedback report on the efficiency and the working of the apps. Plus, the disputes and the bugs will be resolved rapidly, and thus, the products can be introduced rapidly in the market.
11. Integrated Platform
The complete procedure is executed on embedded and integrated platforms. This will make it simpler for the developers to launch the site without difficulty on the customer’s site. Therefore, the implementation procedure will become extra flaccid. The AI-based apps continue to be accepted broadly in the software tests arena and in the future, the technology will assist enhance existing frameworks and tools to target precise issues.
Artificial Intelligence Tools for Test Automation
You can start executing AI in your test automation straight away by exploring current tools. We picked the best tools for QA test automation, monitoring, QA predictions and monitoring, and visual testing.
The software/ tool analyzes the app’s core traits by calculating users’ actions & performing cognitive generation. The app predicts what opportunities a user would be possible to employ and ensures that the product demonstrates the suitable performance, mainly on these vital features. Additional traits count AJAX auto capturing, screen-by-screen analysis, & test case design.
The tool utilizes Artificial Intelligence to predict technical glitches in the well-tested software, navigates 1000s of features in a shorter time period, and provides smart data-driven solutions. The instrument can be connected to any automated engine — it also offers graphic reports on all actions.
The tool structures the functionality of the software automatically and examines varied screens. The crucial purpose lies in developing effective cases to measure each of the scenarios. Test. ai follows and tracks alterations of any component and determines if it was negative or positive. If few traits necessitate manual tests, Artificial Intelligence will assess the situation and alert the QA team straight away.
It is one of the well-known AI-based visual automated test tools. Equipped with robust vision, Applitools can predict users’ activities on the application & summarize the software’s structure. The tool compares the expectations of the users to an existing interface, determines inconsistencies, and adapts to varied browsers and screen sizes.
If you would wish to reduce human engagement in your test automation, you can seek Testsigma — a language-processing software tool that assesses the quality of test cases, so the QA specialists do not have to. The tool will automatically detect overload threats, determine relevant test cases, and control bottlenecks.
In short, the ultimate goal of using Artificial Intelligence in QA testing is to aim for a world where the software will be able to analyze, diagnose, and self-diagnose. This could allow quality engineering and could additionally lessen the test time from mere hours to days. The deployment of Artificial Intelligence in QA testing can save money, resources, and time, and help the testers concentrate their attention on performing the one thing that matters — launch great software.
Software test has significantly changed to enable fast software launches of the superior quality. Many developers have accepted that the no-code/ low-code methods are an effective way to meet the rising demand for faster apps. So why do not we go with the expert’s proposal and follow a new, better approach to testing instead of sticking with the script-based, high-maintenance tests approach. The no-code/ low-code has become a great substitute on which testers or developers can rely on accomplishing this objective. Thus, an advanced approach can also be used to better reach their goals to maximize the scalability of automated tests.
What is Scriptless Test Automation?
STA (Scriptless Test Automation) is a technique that enables business users and testers to automate test cases with zero worries about the coding. It aids to accomplish speedy outcomes and reduces the time spends to know the code. Scriptless Test Automation allows non-test business providers to understand the procedure, examine test cases, and understand what the tester is doing. With Scriptless Test Automation the complete software development procedure can speed up all the while enabling companies to contribute to automated testing and increases the chance of code reusability. By accepting scriptless or codeless frameworks, the development teams could easily spend extra time on the script development. This specific approach will also assist to ease the automated test complication for companies. Hence, Scriptless Test Automation will help companies to reduce the team’s time and efforts, but it will also ensure to maintain good quality and ease in achieving their requirements. Such type of testing will also make test automation easier and simpler to understand and enhance automation test efforts.
According to GlobeNewsWire, “The automation testing market size worldwide is projected to rise to USD 28.8 billion by 2024 from USD 12.6 billion in 2019, at a CAGR (Compound Annual Growth Rate) of 18% during the estimated period”.
Is Scriptless Automated Testing the same as Automation Testing
Test Automation is the technique of automating user testing deeds on an app using an automated tool. The test automation expert uses these test automation tools in developing testing scripts with a programming language. On the flip side, codeless automated test eases the automation testing procedure without real code.
Scriptless test automation is the simplest and effortless means to learn in contrast to other automated tools. The Scriptless automation test aims to solve several discrepancies that test teams have been encountering. Some of the challenges count:
• Testing scripts can be scrutinized and modified only by folks who developed them
• Manual testing experts or engineers can’t automate test cases
• The more the test scripts, the higher the time for implementation
• Maintenance and management of test data and test scripts in an agile environ
Key drivers for the increase in adoption of Scriptless Automated Testing
Some of the core drivers for the increase in adoption of Scriptless Automated Testing are listed below:
Trouble-free adoption of skill set: As codeless automated tools are simple to learn and execute, enterprises can also utilize Business Analysts to execute the automation.
Flaws/ Glitches Prediction capacities: Detecting defects early on and incorporating MI (machine learning) to enhance accuracy in upcoming flaw classification.
Generates Testing Actionable Insights: Using AI to generate actionable insights from test cycles.
Includes model-based Test: Using speech recognition or natural language processing (NLP) technologies for automatic alteration of manual tests cases to automated tests scripts.
Require to optimize time to market: 50 percent higher speed for testing script formation as compared to script tools. Moreover, lessen maintenance time by 80 percent as Scriptless testing is simple to fix and leverage Artificial Intelligence.
Integrated with Cloud & CI/CD: Most codeless/ scriptless automated tools give default integrations with lifecycle tools, in contrast to old tools needing customized development to incorporate.
While accepting Scriptless Automated Testing services, it is crucial to take the significant success factors into concern.
Crucial Benefits of Scriptless Test Automation
• Shortened Automation Time
The better part of this test solution is that test automation could be performed at an early stage in the Software Development Life Cycle, it speeds up the Go-to-Market time. You can begin the test automation procedure way early through the prototypes or wireframes accessible to you. That accelerates the procedure as of least involvement of test scripts.
• Good for Non-Technical and Technical Users
IT experts are aware that something known as a ‘code’ comes into play during tests. They appreciate it very well. But, business analysts put great effort to understand it! Scriptless test automation doesn’t depend on the code. Hence, the QA experts save on time and improve the scalability of test automation.
• Speeds up a time to market
The Scriptless framework is such that it minimizes the time spent on the maturity of scripts, and therefore it becomes likely to develop software programs speedier. Quality Engineers bring together a series of test cases (jointly called test suites) to inspect the behavior of every program. They in fact test for flaws. Fortunately, for them, the feedbacks or results are fast. Positive outcomes guarantee faster delivery of such programs to the marketplace.
When there is barely any complication, prices come down. The test design is easy, and so is the software development procedure. Consumer-friendly test automation eradicates technical requirements. It also minimizes maintenance costs. You can save to a greater extent easily by reducing the expense incurred on scripting huge chunks of programs. Even the test scripts management would be nullified, leading to a strong framework that can be maintained and managed without difficulty with the least overheads expense. In addition, the automation engineers can contribute towards unit tests, white-box tests, and maintains quality infrastructure with DevOps.
• Business Friendly
The participation of expert stakeholders and business analysts certainly gets a boost owing to the scriptless and simplistic nature of the test implementation. The dealings between them and the technical teams become smoother and simpler, ensuring a sure shot superior quality item, which is perfectly tested from distinct angles.
• Fast Scripting
There is no hard and fast rule that there is simply no scripting in Codeless automated testing. However, the easiness and simplicity with which the minimum test scripts have to be written are notable. Not just it fastens the entire procedure; it makes automated testing easily acceptable by the QA teams.
• Trouble-free maintenance of automation scripts
The maintenance phase is said to be the crucial stage of the software development life cycle. Script maintenance turns out to be mandatory even when there is a slight change in existing product traits. Even in the case of false negative or false positive outcomes, a lot of maintenance efforts are needed. But, scriptless automated testing is a hassle-free procedure that necessitates minimal management and maintenance even in large test automation suites. Thus, the test suite becomes highly reliable and in turn, enhances the product delivery time.
Great Tools for Scriptless Automation Testing
Here are some of the better scriptless test automation tools, amongst others:
The codeless/ scriptless automated test and agile test management platform that allows automated web tests. It also enables automation and API validation, which refers to a conglomeration of protocols, tools, and routines that helps the growth of software apps. This amazing tool is compatible with the cloud. Hence, IT teams can easily plan, design, implement and create testing automatically with zero hassles.
Similar to ACCELQ, Test Craft is also good for continuous tests. It is beneficial for regression tests too. IT teams can examine web apps, thus saving on time and costs for maintenance. Best of all, this tool is a free and open-source, automation test framework. The IT arena refers to such a framework as selenium-based. Test Craft tests the validity of distinct web apps amid several browsers and platforms.
Ranorex Studio is an all-rounder! It works perfectly on mobile, web, and desktops. In simple words, this tool is amazingly compatible with the current technological advancements. It allows comprehensive tests, guaranteeing that the app remains suitable for future needs too, and thus loved by both- experts and novices! With the assistance of this tool, it becomes possible to have good control over automation testing during the implementation procedure. Therefore, users gain experience as they go along.
This amazing testing tool is a future-ready automation testing tool that comes with a proven history of delivering some of the better business results. It also accelerates automated test, enhances time to market and generate top-class customer experience. Few differentiators of Nineteen68 comprise 100 percent script less/ codeless automation, simple integration with SDLC products, true thin capacity, business user-friendly, flawless cross-platform test, interactive and extensive reporting capacities, visual plugins that give management and mapping, and so on.
Kobiton codeless automated testing is the raid way for mobile automation testing. This smart automated test platform has functionalities to create and execute scriptless automation on any gadget. Perform manual testing on a single device, and Kobiton’s AI test framework can execute a similar test over other actual devices automatically and in the background. 100 percent Appium code could be easily generated with this platform. It provides the solution for the flaky test scripts through Appium Anywhere.
In short, scriptless automation testing alleviates a noteworthy number of the negative traits linked with automated testing. Scriptless test automation is the perfect fit for projects with tight deadlines. The entire procedure makes automation easy and helps stakeholders to focus on a higher business context. Scriptless Automated testing allows for reliable automation in the fastest time possible. However, to expedite automation testing projects, the engagement of automation experts is vital. Every QA company must concentrate on automation test areas by finding the maximum challenges/ constraint in their SDLC (software development life cycle). The risk of not investing in a specific automation testing solution can lead to a loss in your competitive edge and minimized agility and innovation.
Microservices have crafted highly flexible and adaptable IT infrastructures. Microservices is a unique software development approach that concentrates on creating single-function modules that work jointly to execute the same tasks. It enables you to alter only one service, without modifying the rest of the infrastructure. In simple words, one can easily deploy and change every service without affecting the functional facets of other applications or services. Instead of following an old monolithic architecture (sole app with manifold functions), testers and developers use this microservice approach to build independent modules for every function.
However, the microservice architecture can also make an app extra complicated, particularly when we add several functionalities. Likewise, testing the combined functionality of numerous services is a lot more complicated due to the distributed nature of the app. As microservices follow a dissimilar architecture, we also require an exceptional strategy for testing microservices. In this article, we will explore different tools for testing microservice applications. Testing microservices can assist us in eradicating several issues by avoiding a domino effect.
Three things that one can stress while conducting microservices tests are as follows:
• Code must do what it ought to be done
• Give exact, speedy, and reliable feedback
• Make entire maintenance simpler
Few major benefits of Microservice Architecture:
• Greater Scalability
As the requirement for certain services grows, it is perhaps possible to perform execution on distinct infrastructures and servers to meet your necessities.
• Rapid Delivery
Through distributed development, microservice architecture empowers teams to develop numerous microservices at the same time. Due to the decrease of development cycles, microservice enables execution and updates to be performed more rapidly. Due to this fact, tester teams have extra time to market their product.
• Defined Architecture
As big apps are broken down into smaller parts, testers can rapidly and easily understand, update and enhance those parts; in this way, rapid development cycles are acquired.
The microservice supports independent development, employment, and operation of service. Hence, if an app follows the same approach, any collapse in an individual service wouldn’t affect other services in the app. The services boundaries of every single microservice safeguard failure of the complete app.
• Easiness of Execution
Microservice apps follow a modular approach & every service is smaller than an old monolithic app. As a result, executing a single service is a lot simpler.
Some of the Famous Microservices Testing Tools
There are several tools obtainable for tracking, monitoring, and remediating microservices operation and design as required. Here are few popular microservices test tools commonly used in the industry.
InfluxDBis a free application written in the Go language. It serves as a rapid, trustworthy, and highly accessible database optimized to retrieve time series data. Using this particular tool for performance test microservices can help you discover bottlenecks.
Apache JMeter is one of the highly effective and used performance test tools for testers. It is obtainable as an open-source, which makes it easily available to software businesses of distinct sizes.
Gatling is a microservices test tool written in Scala, which allows it to perform simulations on many platforms. At the end of emulation, Gatling reports on metrics like active user numbers & response times automatically. This particular tool is generally used for testing the performance of microservices and web apps.
Jaeger is an end-to-end, open-source distributed tracing tool that checks and troubleshoots microservices-centric systems. With tracking services across the software’s operations environ, it can carry out root-cause scrutiny, examine key service dependencies and discover areas for optimizing performance.
Hoverfly is an automated, open-source API communication simulation tool that assists with integration tests. The user can test how APIs react to precise events, like rate limits and latency in the network. It also runs test calls between microservices by emulating communications and then records responses and requests in proxy mode to confirm they work as expected.
Pact is a contract test tool that monitors HTTP and message interactions to make sure apps are functioning in a consumer-driven contract manner. In essence, the consuming solutions dictate how the offering services should provide them the data they require. This service then constantly tests to make certain they stay in order with these contracts. Therefore giving a unique test method that has to ideally cut down on large unit testing.
Amazon CloudWatch is a monitoring solution that monitors resource use for apps or microservices deployed on Amazon Web Services. Thus, it can be a helpful tool if you wish to execute load tests for microservices.
Grafanais a free metric visualization & analytics suite. One can use it to visualize time series data to notice how your microservices react under real-time traffic.
Choosing the right microservice testing tools can help you guarantee the best quality of software and bring a result that wins the market. Resolving an issue prior to it reaches the user will enormously increase your team’s and customer’s product confidence, and position your business up for success.
Digital transformation speeds up rapidly, and with higher connectivity, thanks to the fastest WiFi and amazing 5G and improvements in ML and AI. The Internet of Things looks set to deepen its roots in our industries and lives. With more than 30 billion connected devices, the IoT has primarily changed the model of interaction between intelligent solutions, real-life objects like home appliances, and electronic gadgets, assisting us to improve our daily life. As technology is in its golden age, more and more companies will see IoT as a beneficial tool; this will result in mass adoption.
‘Market Snapshot- The Internet of Things’
• The Internet of Things tests market scenario was valued at $ 1107.2 billion in 2020 and projected to hit $ 6042.45 b by 2026 & rise at a CAGR of 32.34 percent over the prediction period (2021–2026). The usage of IoT tests using modern technologies has led to the highest use of distinct kinds of test tools for distinct purposes, and the market is projected to grow at a speedy rate during the period time.
• Microsoft market research says, approx. 94 percent of companies will use some sort of the Internet of Things by 2021. Core IoT verticals like retail, government, manufacturing, healthcare and transportation continue to launch new IoT solutions and apps to their everyday operations.
• As per Statista, the total dollars spent on IoT solutions globally is anticipated to almost double in 2021, up to $418b from $248b. Surprisingly, by the year 2025, that number is projected to be USD 1,567 billion. The international market for IoT end-user solutions is projected to raise to 212 b U.S. dollars in size by 2019. The technology hit USD 100 billion in market revenue for the first time in 2017, and predictions proposed that this figure will rise to about 1.6 trillion by 2025.
• Fortune Business Insights report says IoT technology holds important potential in the ICT segment with the worldwide market valued at $190 b in 2018 and hitting $1.1 trillion by 2026. The report projects that the international market will grow at a surprising CAGR of 24.7% all through the estimated years.
Progressively more organizations will recognize this potential this year, assuring development for this trend. Let us dive a little deeper and explore the IoT-related trends that will shape the digital transformation approach for businesses in 2021.
7 Biggest IoT Trends to Watch Out for 2021
1. Focus on Security to meet the Complex Challenges
Security has become a foreseeable matter nowadays, and with up-and-coming technologies, companies need to make certain the data security to retain their customer’s interest. Hence, IoT is expected to concentrate on security to meet the complicated challenges in the coming decade. With various devices, IT administrators are hostile to know how many gadgets are usually connected to their networks leaving them susceptible to attacks. Besides, connected devices remain susceptible owing to exposure to cyber-attacks. The amount of Internet-linked gadgets has shown a notable rise, and they will keep mounting in the coming decade. Therefore, extra cautions of network operators can easily stop intruders to enter the network making IoT security the most modern IoT trend.
In 2021, we would likely see an increase in the security-centric smart gadgets, counting AI-driven, automatic capacity to scan networks for IoT gadgets. Big tech enterprises are expected to lead the way in this arena. Big giants like Amazon recently announced a chain of new traits that allow users to take control of privacy and data settings. Apple and Google are also in the same race to follow suit in 2021 with a focal point around the security features marketing in IoT-centric devices.
2. Artificial Intelligence meets IoT
In addition to security, the focus is also moving to the holistic improvement of production procedures. In simple words, the combination of advanced technologies into an “Artificial IoT” mainly reduces deviations from the optimum in the manufacturing procedure and therefore ensures high performance, lower costs, and less waste. Through AI, production processes can be continuously and automatically optimized with the assistance of ML methods. Besides, AI-driven analytical solutions have the control to aggregate huge amounts of sufficient high-quality data and information; process it in real-time and draw effectual insights. Moreover, close integration of AI, smart devices, and Big Data will also contribute appreciably to give protection against security risks. So far, merely a few businesses have deployed AI Internet of Things. That will change this year.
3. Enhanced Role of Data Analytics
Data analytics plays a crucial role in well-organized and effectual business management to make a significant decision based on a detailed analysis of the gathered data. The modern AI-centric data analytics solutions, powered by Big Data technology and AI algorithms, can collect a huge chunk of information, examine it in real-time, and derive valuable insights from it. This powerful incorporation of Big Data, Artificial Intelligence, and IoT devices will allow users to make important and effectual business decisions with ease based on the information & insights collected by the data analytics. IoT not only aids in examining behavior and spit out data; it is also about rapid data processing and giving proposals based on those findings.
Leveraging data analytics will complement the data scrutiny produce and process by the internet of things solutions. When executed correctly, data analytics will allow users to pick up on trends or patterns within the information gathered by their devices. Consequently, the insight acquired by the data analysis confirms a business is well equipped with the data required to make
4. Blockchain Technology
IoT devices very often are susceptible to security breaches that make them target for DDoS attacks. Blockchain technology or distributed ledger technology emerges as a suitable tool to guarantee data safety during encryption techniques plus peer-to-peer contact without intermediaries. It is amongst the top-most IoT trends that address major IoT scalability and security challenges. Credited to its exceptional capacities and advantages, Blockchain is an information game-changer, giving a means for data to be recorded and shared by a user’s community. It is more often looked upon in the context of IoT data security.
It has become the norm for banking or financial institutions to guard their operations with the aid of Blockchain technology. Similarly, blockchain is at present amongst the top IoT trends due to its capability to confirm data protection through encryption techniques without intermediaries.
5. Emerging IoT Apps
Apps and use cases of the Internet of Things solutions are evolving at a fast pace. Presently, its apps surround smart homes, smart grids, wearable, smart cities, industrial settings, etc. With the rise and development of this technology in the upcoming future, the Internet of Things will reach more business and industry settings, leading the globe towards more digital. Knowing the Internet of Things use cases will assist companies to integrate the Internet of Things technologies into their upcoming investment decisions.
6. Edge Computing
What is edge computing? With this a distributed computing paradigm, rather than the Internet of Things devices sending all the information they gather to the cloud for investigation and extraction of insights, this work is performed straight on the devices themselves. Adoption of edge computing will become more significant for the Internet of Things devices to conquer the cloud computing drawbacks such as latency issues and low bandwidth faced in real-time data processing. Edge computing is an accurate data processing and cost-efficient method for IoT devices.
Companies should make decisions based on IoT information speedier than ever before to appreciate the true devices value on the network. With the union of 5G networks, an increase in IIoT and IoT devices, and a striking increase in the data amount we are collecting, edge computing can be turned up as significant as ever in this year.
7. Investment in IoT App Testing
Smart sensors, wearables, and connected devices will continue to alter the way healthcare is delivered, from automated homes to telemedicine help for the disabled and elderly. Besides, in situations where the risk of virus infection is strong, it will also be used to minimize unnecessary contact.
The IoT testing is all set to incorporate with other technology to make life smart and easy. If we speak about the IoT role in the banking industry or its penetration into healthcare services, growth in this technology will continue to bring great deeds across the globe. The IoT trends would bring the world together and make it victorious in every way.
As time changes, the future of IoT app tests will continue to grow. We would have the Internet of Things attached to more or less in all inventions. Let’s get ready to observe this year the creativity of automatic urban societies working with each other with zero contact.
2021 promises to be another year of trouble and uncertainty around the globe and IoT technology will undoubtedly deliver practical solutions to various complexities and challenges faced by people working tenuously.
The popularity of AI has created high hopes, and of course, QA and software tests too have not remained exempt from the charms of Artificial Intelligence. AI brings new and creative Intelligence to everything it touches by using the vast ocean of data at hand. Tech giants like Google, FB, Amazon, Microsoft, and the like spent huge money on their AI initiatives. Influential voices also started speaking about the paradigm alteration that this technology would bring to software development. From independent tests to continuous tests, Artificial Intelligence has transformed the software testing and development industry. AI is the key to streamlining software development and testing and making it smoother and more efficient.
Gartner report says, by 2020, Artificial Intelligence technologies will be universal in almost every new service and product and will also be the investment’s prime concern for CIO’s. Even 2018 were all about AI.
Some 21 per cent of IT leaders surveyed stated they are putting Artificial Intelligence trials or proofs of the model in place, as per the 2020–21 World Quality Report. Speaking to extended-term trends, only two per cent of respondents said Artificial Intelligence has no role in their upcoming plans.
Software testing and QA remain significant cost for software firms (it took more than a quarter (26 per cent) of IT expenses the previous year) — so there is an enormous incentive to spend on automated testing platforms that are AI-powered.
What does Artificial Intelligence (AI) mean for Software Testers?
It is a true statement that Artificial Intelligence shows enormous potential to detect test bugs or glitches rapidly with zero human intervention. Just as automation reduces monotonous manual work for software engineers, Artificial Intelligence also aims to minimize tedious work with extra intelligence. It seeks to find solutions to issues in the future by learning the answers to the same issues faced in the past. Software engineers have to perform what they are doing continuously. Yet, they also know AI-based automated testing tools and use them to improve their efficiency.
With Artificial Intelligence, QA testers will transform from being a software tests team to an automated tests team because Artificial Intelligence will make QA tests extra efficient. With around 70 % of testing being recurring, AI can rapidly occupy the space, and several things will change in the tests field. Hence, it can considerably increase the overall quality quotient of an item to be launched in significantly little time if software testers efficiently use AI tools.
IBM’s Systems Sciences Institute stated that the price to fix a bug observed after product release was 4–5 times more costly than one uncovered at the time of designing — and 100 times more than an error detected in the maintenance stage.
How costly can bugs turn out to be if they are not identified earlier?
• USD 10,000 — Production
• USD 1,500 — QA testing phase
• USD 100 — Gathering Requirements phase
Games developer Ubisoft has launched an AI-based tool that can alert software developers to possible bugs when they type code. Bug fixes can take up 70 per cent of the development budget of Ubisoft for a game. Thus, Artificial Intelligence stands to proffer remarkable financial rewards for their business.
9 Advantages of using Artificial Intelligence in QA and Software Testing
1) High level of Accuracy
Manual tests are accurate but not error-free. Sometimes, the possibility of errors or bugs remains unnoticed by QA engineers. Test automation helps in performing the same series of actions without missing the details. The QA engineers use automated software to complete the recurring test.
2) Better Defect Tracking
In old tests methods, bugs or errors remain ignored for a long. These unnoticed bugs become a nuisance later on. Artificial Intelligence can catch flaws in seconds. Artificial Intelligence analyses these bugs. As test data grows, so do the no. of errors or bugs. Artificial Intelligence automates procedures, so codes are corrected automatically, and software test teams would smartly perform bug tracking. Artificial Intelligence takes fingerprints of failures on debugging logs and detects duplicate errors.
3) Better Flexibility
Even the most accessible modifications in an app can lead to testing failures in automated testing tools as traditional tests scenarios consider a sole path or selector. Thus, such kind of test approaches is someway rigid. ML and Artificial Intelligence allow for a highly flexible tests procedure, knowledge relationships between several documentation elements. Such systems can adapt automatically to any alterations in real-time, being both reliable and flexible.
4) Controls Tests Time
Artificial Intelligence in QA testing minimizes the time spent on manual tests. QA teams can easily apply their efforts to highly complicated jobs that require human interpretation. QA staff and Developers would require using small attempt to design, prioritize, write, and manage E2E tests. Hence, it will accelerate timelines for release and free up resources to develop new products rather than tests a fresh release.
5) Enhanced Regression Tests
With progressively more rapid deployment, there is an accelerated requirement for regression tests, to the point where humans can’t convincingly keep up. Organizations can use Artificial Intelligence for few tedious regression tests tasks and use Machine Learning to create test scripts. For instance, in case of a User Interface change, one can use Artificial Intelligence /Machine Learning to scan for size, shape, color, or overlap. These would otherwise be manual testing; we can also utilize Artificial Intelligence to validate the alterations that a software tester may miss.
6) Consistency in Testing
While QA testers are good at detecting and addressing complicated issues and proving testing scenarios, they are still human. Bugs can occur in tests, particularly from burnout syndrome of finishing monotonous processing. Artificial Intelligence isn’t affected by duplicate tests and hence yields more reliable and accurate outcomes. Often, grudges happen between QA analysts and developers, chiefly under time constraints or the results found during tests. AI/ ML can remove such human interactions that may lead to holdups in the tests procedure by giving objective results.
7) Enhances Test Coverage
The well-detailed nature of test automation enhances the software tests procedure. Artificial Intelligence helps QA engineers to check memory, the internal program states, files storage and content, and data tables. AI finds patterns and examines the database behaviour as per expected results. AI in software testing can carry out various test cases in single testing, providing a comprehensive coverage set.
8) Release software in small timelines-Faster Time to Market
Automated tests assist in reducing the development and tests timelines. In test automation, a test gets implemented after each source code alteration. It decreases any extra cost involved to run recurring test cases. Artificial Intelligence integrated software test proves to be accurate and time-saving. In the QA and software test industry, time is straight proportional to money.
9) Executing Visual Tests
Image & pattern recognition enables Artificial Intelligence technology to identify visual bugs by executing apps’ visual tests. Artificial Intelligence can separate dynamic User Interface controls despite their shape and size, assessing them at the pixel level.
Top 5 AI-based Automated Tests Tool
It is a cloud-based tool which is beneficial for function, performance, and load tests purpose. This test tool is a one-stop solution to every problem and uses AI and ML to speed up test creation, analysis & management. The good thing is that you need to type what you wish in English, and natural language processing will generate the functional test cases automatically. Surprisingly, it implements 100s of tests in few minutes from every desktop as well as mobile browsers.
It is the best tool that uses Artificial Intelligence technology to generate test cases depending on consumer behavior. The testing portfolio explains exactly what real systems will perform on the production systems. Thus, this testing tool makes it 100 per cent client-centric.
It is one of the perfect automated test platforms for regression and continuous tests on selenium. Testcraft can also be used to scrutinize website apps. Artificial Intelligence plays a vital role in avoiding time and management cost by automatically defeating the app’s moderations.
It is one of the best tools for visual User Interface tests, software monitoring, and visual management. This test tool scans app screens and examines them just like the humans’ brain; however, with ML. DevOps, automated testing, manual QA, and digital transformation specialist’s teams can easily use AppliTools.
Artificial Intelligence is the next big thing; however, it would not replace humans. Artificial Intelligence will execute various tests rapidly. But humans will still manage the result of the testing, as Artificial Intelligence can only perform definitive tests, whereas latent and implicit testing has to be executed by Human testers. QA testers will observe the more exciting parts of QA tests by working in harmony with Artificial Intelligence. The QA engineers working hand-in-hand with Artificial Intelligence and can revolutionize the way we test now.
SMEs could easily benefit from software testing using Artificial Intelligence/ Machine Learning to meet the tests team’s crucial challenges. While AI and ML are not substitutes for human testers, they could be an add-on to the testing tactic.
Ever since the technology and procedure have progressed a lot, organizations or teams prefer to have quicker testing feedbacks. Since we can notice with the shift-left trend to DevOps, Application Programming Interface testing has become a significant and crucial factor on CI/CD. Automated testing efforts can bring extra worth with the right API testing in place, rather than depending on time-consuming User Interface testing only. The interest in API tests has been growing progressively over the last few decades, as per Google Trends.
Why API testing is required?
• Scrutinizing an application at the API level would be catastrophic thus it is better to do it at first
• Main functionalities of the Application Programming Interface can be validated
• Consumes less time than that of Graphical User Interface functional tests
• Testing data is mostly derived as XML or JSON. Thus the procedure, not language dependant
• Can be easily integrated with Graphical User Interface tests
Top 9 API Test Tools to Look Out in 2021
Postman being originally come to the market as a Chrome plugin, is now expanding its solution with the native version for Windows, Linux, and Mac. It is a great option for API tests for those who do not wish to deal with coding in an IDE (integrated development environment) using a similar language as the software developers. So, whether you are searching for manual or exploratory testing, it is a great choice.
With the help of this tool, you can create automated tests, you can monitor the API, execute debugging, and run requests
Its interface enables users for extracting web API data
Supports Continuous Integration/ Continuous Delivery service with Newman
Postman allows writing Boolean testing & is not based on the command line
Counts built-in collections, tools, & workspaces
Supports several formats, counting Swagger and RAML
Can be used for both exploratory and automated tests
It is one of the most popular API tools that facilitate simple tests of REST services. REST Assured is an open-source or free tool and a Java domain-specific language perfectly designed to make the REST test easier. Besides, the newest version has fixed OSGi support-concerned problems. It also provides additional assistance when it comes to making use of Apache Johnzon. Starting with version 4.2.0, this tool requires Java 8 or higher. It is bundled with countless features, allowing users to continue tests without any coding.
Supports any HTTP technique but has an explicit hold for several kinds of commands like PUT, POST, DELETE, GET, PATCH, OPTIONS, and HEAD & comprises specifying as well as validating e.g. headers, parameters, cookies, and body effortlessly
Built-in functionalities make sure that users do not require to perform coding from scratch
Users do not require an extensive understanding of HTTP
Support BDD Given/ Then / When syntax
The sole framework can have a blend of REST tests and User Interface
Flawless integration is possible with the Serenity automated framework
Introduced to perform load testing, this tool is now well-liked for functional API tests. Moreover, JMeter 5.4 brings on in Dec 2020 with extra core enhancements and bug fixes. The user experience is also far better than the last versions. The latest release is JMeter 5.2 in Nov 2019. The JMeter has been packed with varied enhancements and features, several bug fixes, and enhanced user experience, like JMESPath extractor, new protocol, JDBC improvements, HTTP Samplers, and StringtoFile.
JMeter is compatible with dynamic and static resources to test performance.
The integration between Jenkins and Apache JMeter enables users to include API tests within Continuous Integration pipelines.
Automatically works with CSV files and enables teams to create unique parameter values for tests.
It is a very flexible API testing tool and assists in customization as per the tester
Supports manifold protocols for an effective test process
It is one of the robust web services test tool giving the edge of the CD feature. Tricentis accelerates test with a script-less, no-code approach for E2E (end to end) automated testing. Tricentis’ 400+ users comprise global names from the Top 500 brands like Whole Foods, ExxonMobil, HBO, BMW, Toyota, Allianz, Deutsche Bank, Starbucks, Lexmark, Orange, Vodafone, A&E, Vantiv, UBS, and Telstra.
Supports a wide range of protocols counting AMQP, HTTP(s) JMS, TIBCO EMS, SOAP, IBM MQ, Rabbit MQ, NET TCP REST,
It integrates into the DevOps and Agile Cycle
Best API automated tools which use model-based automated testing that makes script maintenance simple
Enables E2E test as API tests could be used amid packaged apps, mobile, cross-browser, etc.
The popular and most widely used tool for API tests in the world, SoapUI enables you to test SOAP and REST APIs with no difficulty — as it has been built especially for API tests. It is a test automation tool for REST and SOAP APIs. SOAP UI acts as a headless functional test tool dedicated to API test and using this tool allows users to get the full source and build the preferred traits besides these capabilities. SoapUI Pro is used by 1000s of renowned companies across the globe, counting: Microsoft, Apple, Cisco, HP, Oracle, eBay, NASA, FedEx, MasterCard, Pfizer, and Intel.
Fast and Simple Test Creation i.e. drag-and-drop, point-and-click, functionality makes complex tasks (such as working with XML and JSON) easy
Robust data-driven test that is load data from databases, excel and files to simulate the way users interact with the APIs
Reuse the functional test cases as security scans and load testing in just a few clicks
Flawless Integrates with thirteen API management platforms, supports SOAP, REST, IoT, and JMS
Identify performance problems by tracking API traffic, error rates, and response times,
Simply generate API proxies from the Open Application Programming Interface Specification and employ them in the cloud
On-premises (in a private cloud) or cloud and often using a hybrid deployment model
SOC2, HIPAA, PCI, and PII for applications and Application Programming Interfaces
Apigee is purpose-built for online business, & the data-rich mobile-driven Application Programming Interfaces and applications that power it
Its full lifecycle Application Programming Interface management platform gives the dashboards, visualization tools, and reports to assist measure the data that flows across Application Programming Interfaces in real-time
Manages the procedure to design, develop, publish, deploy, version, governance, monitor accessibility, and measure performance
This is a well-accepted API testing tool that constantly tests web services and concentrates on reliability and automation. In October 2019, this tool introduced the newest trait called Encrypted variables, which offers a fresh way to store passwords, tokens, and confidential data fields requisite by tests to advance API test security practices. Encrypted variables aren’t merely trivial to use, however, build on the cryptographically sound method for safe storage.
Supports automation Application Programming Interface tests through every single step of a CI and CD pipeline
Supports running Application Programming Interface testing after deployment
It can be integrated with some of the other tools like Zapier GitHub, and Slack
Support authenticating Hypertext Transfer Protocol (HTTP) reactions with turn-key assertions like JSON Path data integrity checks and JSON Schema validation
The synchronize feature allows users to upgrade tests when their specifics change, you don’t have to manually update their tests after adding-up new parameters or modifying the response of the Application Programming Interface.
It is an API testing tool that enables users to begin their security, performance, and functional test straight from the Open Application Programming Interface Specifications. Swagger tooling & Ready Application Programming Interface platform make it simple to rapidly generate, manage, and implement Application Programming Interface tests in the pipeline. Open Application Programming Interface Spec version 3.0 in March 2019 came with the newest traits Swagger Hub Domains. With this amazing trait, software developers can take commonly used objects, path items, response, and accumulate them in individual files to be referenced amid manifold distinct Application Programming Interface definitions. Such re-usable Domains can be published, shared, and versioned for collaborative feedback amongst big teams.
Swagger Inspector offers capacities to inspect Application Programming Interface request-responses and ensure they execute as expected
Import user’s Application Programming Interface definitions to automatically generate assertions against endpoints, validate schema rules & insert synthetic data into parameters with zero trouble
Generate complicated load scenarios for testing the scale and performance of the Application Programming Interface easily
Support every kind of services from GraphQL, SOAP to REST,
With the Application Programming Interface test, once the logic is designed, testing can be built to authenticate the correctness in data and responses. We do not need to wait for several teams to end their work or for complete apps to be built — test cases are isolated plus ready to build straight away.
• Simple Test Maintenance
User Interfaces are continually changing as well as moving around based on how they are accessed — screen orientation, devices, browsers, etc. This forms a nightmare scenario where testing is being continuously rewritten to continue with the real code in production. Application Programming Interface changes are very much controlled and infrequent — sometimes Application Programming Interface definition files such as OpenAPI Spec can aid to make refactoring testing only seconds of work.
• Rapid Time To Resolution
When Application Programming Interface tests fail, we know accurately where our system broke plus where the imperfection can be found. This aids in reducing time triaging bugs between integrations, builds, and even distinct team-members. The isolated, small footprint of an Application Programming Interface testing is perfect for rapid MTTR stats, a worthy KPI for DevOps groups.
• Speed & Coverage of Tests
Three hundred User Interface testing may take 30 hours to execute. Three hundred Application Programming Interface tests could be executed in three minutes. That means you will search for more bugs in lesser time, whilst also being about to fix them straight away.
Out of the above-mentioned top API test tools, Postman, SoapUI, and Katalon Studio provide free and paid plans. Whilst JMeter, REST-Assured, and others are free or open-source tools that are accessible free of cost.
There has been a great advancement and growth in the sector of QA testing with the latest trends introducing into IT field services. The introduction of innovative technologies has brought the newest updates in software testing, development, design, and delivery. The high priority of enterprises around the world is cost optimization. In undertaking so, the majority of the IT leaders believe in the incorporation of the newest IT methodologies for their organization. Digital transformation is yet another significant focus point for the sectors and the enterprises that are ranking top on cloud and business analytics.
Even automation practices became the mainstream, paving the way for flawless test practices. Besides, AI and ML seem to reach a new level. Nowadays, Big Data tests mainly include data testing, paving the way for the Internet of Things to become the center point. It is one focal point where all software testing companies should treat with care. Factors such as reliability and quality are being given extra attention that results in the decrease of software app errors, enhancing the security and the app performance.
Modifications in the trends of software testing would also have a significant influence on software tests and QA. The enterprises have increased their budgets for software testing, particularly in the industries of utilities, transportation, and energy. Nowadays, the enterprises are incorporating their testing, earlier in the SDLC (software development life cycle), with test methods such as Agile. This also includes the institution of the T-CoEs to go with the mechanism of the tests with business development building items that are ‘All set for Business.’
Some organizations are also hiring independent testing companies to meet their software testing requirements. In this mode, they incur less cost on QA and tests and don’t even necessitate in-house resources. There are various other vital trends in the Quality Assurance and software-testing arena. Therefore, there is a strong requirement to adapt the most emerging testing trends for all the software enterprises globally, which will assist them in adapting to the needs of the current advanced world. This article will help you to explore some of the top-most Software testing trends to look out for in 2021.
Software Testing: ‘Upcoming Technologies’
The technology scenario in software testing is changing. Latest trends are more appropriate than ever for enterprises and testing experts, as modern users live in the ‘always-on’ manner and necessitate everything accessible. As the no. of apps organizations use grows, and as security and safety-allied costs increase, software test now receives extra attention than ever before, and for a better reason.
As per the World Quality Report, 60% of companies list cost as the highest test environment challenge. The overall test budgets have become more and more inseparable from software engineering resources and budgets. Quality Assurance is now further embedded in the development cycle due in part to the increase of practices such as continuous testing and DevOps. As a direct consequence and effect, more and more companies are beginning to appreciate the worth of QA by seeking software testing and QA consulting companies to aid them with this dedicated work.
While Artificial Intelligence is a useful tool that makes test automation tools and QA actions, in general, more capable, it in no way negates the requirements for skilled testing experts who can develop a lucrative, quality testing solution. Moreover, user testing with real human beings is still a vital element to make sure your product is working, valuable, and user-friendly to your customer.
Top 15 Software Testing Trends in 2021
1. Codeless Automated Testing
The higher adoption of codeless testing tools will be the main software testing trends in 2021 to watch out for. Codeless testing tools are built on sophisticated AI technology plus visual modeling allows the faster formation of test cases that cater to automated tests. Using such tools, IT employees can generate easy test case scenarios with no coding know-how and reduce the time wasted on recurring test cases.
Some of the crucial advantages of codeless testing are effectiveness, ease to review, low learning curve, and saving precious resources. In short, all these reasons combined means that, with codeless test automation, there is no pressing requirement to understand automated testing frameworks or the technology underlying an app to be able to test automatically. Out of the blue, the route to success with automated testing seems within reach. Automated testing tools like Selenium built on this visual approach and also empower even the non-developers. With time, other traits were added, such as RC, IDE, webdriver that added to its significance and value. ‘Selenium IDE’ made persons who did not wish to get indulged in coding. Selenium presently supports varied programming languages such as Python, Java, Ruby, and C# and so on. It allows them to create, manage, and implement automated tests themselves without having to learn how to code.
How does Codeless Automated Testing work?
Codeless automated testing automation is the same as codeless software tests. The basic principle of codeless automated testing is that the test creation shouldn’t necessitate any sort of coding. Now, as there are a plethora of tools accessible in the marketplace that provides codeless test automation, there are varied means of how it works on the Frontend. The most common procedure for them is the alteration of frontend illustration to machine meaningful code in the backend to ultimately make it function.
For instance- In the case of a tool like Testsigma, the test cases are majorly written in an easy language like English, using NLP. These reports are transformed to code (in the backend) for implementation.
Below are a few more popular test automation tools that use codeless testing techniques to allow the automation of test cases:
TOSCA: This amazing tool by Tricentis uses a model-based test methodology. Previously test creation entails having a model of the app under test, test data, and test scenarios in place. Here too, slight modifications in the app are corrected automatically.
test.ai: It is one of the most popular automated tools that test your mobile apps for user experience automatically. There is no necessity for coding as well as maintenance here. It runs on AI that studies an app after that automatically generates test cases; execute them to give the outcomes related to user experience.
Ranorex: This tool offers a package of numerous solutions in one and an amazing trait for the easy recorder for recording and playing.
Ghost Inspector: Each move in this tool can be created without the necessity of any coding. The tool makes it easy to ensure your website is properly working.
TestComplete: The specialized tool from smartbear, they make use of keyword-driven tests for automation and no code.
2. ML and Artificial intelligence Adoption for Test Automation
The rising demand for Artificial Intelligence continues to grow due to the growing number of apps we use in our linked world. Current expense in AI is projected to be $6–7 billion in North America single-handedly. By 2025, AI global investment, in general, is going to hit reach $200 billion.
Some Artificial Intelligence (AI) Statistics at a Glance!
Nearly 64.8 percent of firms have invested over USD 50 million in Artificial Intelligence (AI) and Big Data initiatives in the year 2020, up from 39.7 percent in 2018. — Forbes
In 2020, 37.8% of industry-leading companies generated a data-driven company by leveraging Artificial Intelligence (AI) and big data. — Statista
Computational resources used in Artificial Intelligence will grow 5x from the year 2018 to 2023. It will make Artificial Intelligence the top-most group of workloads impelling infrastructure assessments and decisions. — Gartner
The most popular AI-based automated testing tools are as follows:
Appvance: This tool uses AI for generating test cases based on user behavior. The portfolio of tests systematically covers what genuine users do on production systems. Therefore, this makes it 100% customer-centric.
Testim.io: This tool uses Machine Learning for the authoring, implementation, and continuity of test automation. It emphasizes user interface testing, comprehensive testing, and functional testing.
Test.ai: It is one of the popular mobile test automation tools that use Artificial Intelligence to execute regression tests. This tool is beneficial when it comes to obtaining the performance metrics on your app and is a better monitoring tool than a functional test tool.
Functionize: It used ML for functional tests and is similar to different test tools available in the market regarding its capacities like being able to run tests rapidly (without scripts), carries out multiple tests in a few mins, and executes deep analysis.
TestCraft: It is an AI-based automated testing platform for continuous testing and regression testing that functions on top of Selenium. TestCraft is also used for monitoring web apps. The role of AI-powered technology is to eradicate cost and maintenance time by overcoming modifications in the app automatically.
Applitools: It is one of the most popular app visual management and AI-powered visual User Interface monitoring and testing software. It gives a comprehensive software test platform based on Visual AI and can be used by experts in Digital Transformation, test automation, engineering, DevOps, and manual QA teams.
Sauce Labs: It is also one of the best cloud-based automated testing tools that leverage AI and ML. This amazing tool supports a comprehensive list of OSs and browsers, mobile simulators and emulators and mobile devices, and at the pace that its users require to test their applications.
3. Test Automation in Agile Teams
Agile testing and agile development are rapidly increasing in popularity, and intelligent Quality Assurance or test teams keep pace with present growing software trends. Agile tests tools differ from project mgmt tools to test automation tools. Any Agile project with no test automation is effectively a waterfall project in phases. Automated testing is considered a crucial activity for agile methodologies being the major drive to accelerate the QA procedure. As per the latest report of MarketsAndMarkets.com, ‘the automated testing worldwide market size is expected to hit from $12.6 Billion in 2019 to $28.8 Billion by 2024 at 18.0% of a CAGR (Compound Annual Growth Rate) in this estimated period.’
4. Increase Demand for Big Data Testing
Companies across industries continue to cope with immense data volume and different data forms. The mining of any amount of unstructured or structured data defined as big data requires an end to end testing. Big data tests assist in making enhanced decisions with correct data validations and improving business strategizing and market targeting with well-versed decisions drawn from this big data analysis.
According to MarketsAndMarkets, the big data market global value is estimated because of the increased IoT devices usages in enterprises and the higher initiatives by the governments to boost the use of digital technology. Higher dependency on data amid every vertical requires successful big data testing to guarantee integrity, accuracy, reliability, and quality of data necessary for making informed decisions by all enterprises. Particularly, big data tests aid in making data-driven decisions about several services and products, which are captured and scrutinized to deliver important insights to enterprises.
5. IoT Testing to Boost Digitally Connected Smart Devices
The number of interconnected gadgets reached 20 billion by 2020 compared to the data of 6.4 billion during 2016. These stats represent the enormous expansion and the requirement for an effective IoT test strategy. This IoT testing counts the test of communication protocols, operating systems, together with hardware and software of the IoT devices. There is a possibility of risks seen in the hardware of several IoT products, which is vulnerable to several threats that require to be efficiently tested. Also, the software is in-built into the IoT devices. Therefore, it is essential to get all the IoT gadgets and security tested to avoid vulnerabilities and threats. The majority of companies have already started determining the necessity for an effective IoT test strategy to allow well-connected and efficient smart devices required for end-users.
What does Market Stat say?
The IoT Testing market was previously valued at $ 781.96 billion in the year 2019 and is projected to hit $3624.23 billion by the year 2025, at a Compound Annual Growth Rate of 32.34 percent over the prediction period 2020–2025. The usage of IoT tests using advanced and leading-edge technologies has led to the rising use of distinct kinds of test tools for several purposes, and the market is projected to grow at a rapid rate during the forecast period.
6. Rising Adoption of Agile and DevOps
Companies have embraced DevOps as a response to the demand for accuracy and speed and Agile to respond to quickly changing needs. DevOps comprises practices, processes, tools, and rules that assist in integrating operation and development actions to minimize the time from developing to operations. DevOps has to turn out to be an extensively accepted solution for enterprises that are looking at means to shorten the SDLC (software development lifecycles) from development to operation and delivery. The increasing adoption of both DevOps and Agile helps the QA experts to develop and send quality software rapidly, which in turn is also referred to as ‘Quality of Speed.’ Such adoption has gained higher interest over the past 5 yrs and continues to strengthen in the coming years.
7. Switch to Performance Engineering
Attaining higher performance was a considerable job earlier whilst developing the software. You require working on several elements, such as business value, usability, simple configuration, and security. The various platforms on which the app is made obtainable describe the captured user experience as well as market size.
User experience plays one of the significant roles during short development cycles, frequent releases, as well as changing market needs. In response to this trend, software developers started prioritizing a customer-focused approach at each SDLC phase to reduce performance glitches and bottlenecks at the earlier stage of the product’s lifecycle. Accordingly, performance test goals have transformed into scrutinizing the inadequate performance of the system and knowing where it is rooted in the software development procedure. Hence, in response to that, performance engineering is developed as a replacement for performance tests for building significant performance metrics from the very first design.
A few key differences between performance engineering and performance testing:
• First and foremost, performance testing is a quality check of the app’s responsiveness as well as load handling. It establishes how perfectly the system will tolerate a production load and foresees the glitches that could turn up during heavy load situations. However, Performance engineering seeks out for designing the app from the beginning with performance metrics such as turnaround time, quality, productivity, etc., in mind and facilitates the early detection of problems in development.
• Second, the performance test is a Quality Assurance procedure that generally takes place when a software development round is finished. Performance engineering, on the flip side, is a nonstop process that is entrenched in every stage of the software development cycle, starting from product design to development, and then to the end-customer experience.
• Lastly, the performance test is conducted by the software testing team, while performance engineering involves teams of Quality Assurance and RND.
8. Blockchain Testing
Blockchain technology is necessary for businesses like cryptocurrency, automotive, and finance. It makes a decentralized network distinct from a central system utilized by traditional banks for managing banking as well as financial operations. So, undeniably, blockchain technology has transformed the method enterprises are dealing with digital currencies like bitcoin. These blockchain apps aren’t just constrained to the financial world, and their smart contracts are being employed in each business field, from the governmental services to the energy vertical. The extensive range of blockchain apps, however, brings in several challenges to blockchain debugging. Blockchain testing is an efficient, specialized, and next-gen testing solution to debug the code to deliver productive blockchain apps.
As per Marketsandmarkets, the international blockchain market size is projected to hit $39.7 billion by the year 2025 from $3.0 billion in 2020. By 2022, it is anticipated that a blend of blockchain technology and IoT devices through smart contracts will allow micro-transaction between the two different parties, which will be an upcoming trend. Moreover, the Australian Securities Exchange is also planning to employ a new blockchain-centric system for managing the Australian financial market by the 2020 year-end. Besides this, a current PWC report reveals that 77% of financial institutions are likely to accept blockchain technology as a section of an in-production procedure or system by the year 2020. Such stats decipher the increasing scope of blockchain technology and the requirement for blockchain tests.
Blockchain Testing: Core Testing Types
Some of the key testing types that must be run include performance, functional, node testing, API, and other specialized testing.
• Performance testing: Performance testing determines performance bottlenecks, proposes the techniques of fine-tuning the system, and reassesses if the app is ready to launch in the market.
• Functional testing: Functional test is a holistic procedure that estimates the work of several functional portions of the blockchain (for instance- smart contracts).
• Node test: Every heterogeneous node on the network should be independently and perfectly tested to guarantee smooth cooperation.
• Application Programming Interface tests: API testing addresses the interface between apps in the blockchain arena. It examines to make certain that Application Programming Interface replies and requests are handled and formatted appropriately.
Some of the Most Popular Blockchain testing tools:
Ethereum Tester: This is one of the most utilized platforms and open-source test library available as a Github repo. Ethereum Tester’s installation is pretty simple, with a manageable Application Programming Interface support for several testing requirements. It is reliable for Web3 Integration, API, smart contracts, backend, and various other blockchain testing.
Ganache: Earlier named as TestRPC tool, it is exclusively built for testing Ethereum contracts locally. It generates a simulated blockchain that enables any person to use several accounts to test.
Populus: This framework developed around the py.test framework has the testing functionality of Ethereum entrenched in the shape of a series of features for testing contract deployment.
BitcoinJ: This is also a famous tool and a Java-based framework built for Bitcoin-based applications that allow you to interact with the actual BTC network and several test activities.
Embark: Embark is a test framework that concentrates on developing dApps (decentralized apps) that run on several nodes or systems. This amazing framework has integrations with IPFS, Ethereum blockchain, and decentralized communication platforms like Orbit and Whisper.
Truffle: This incredible tool is a commonly preferred name for Ethereum developers. It brings in the best testing features like automated contract tests. It holds capacities beyond merely testing functionality within the Blockchain app.
Exonum Testkit: Tests the operation of the whole service is the strong point of Exonum Testkit. It enables anyone to test the Application Programming Interface and transaction execution in the organized system, i.e., without the association of the consensus algorithm and network operation.
9. Cybersecurity and Risk Compliance
In 2020, cybersecurity testing has been turned out to be a growing trend in Quality Assurance and software tests. The report summarizes some key objectives goals that explain its inclusion as an individual topic: growing awareness of the importance of security among all industries, increasing the product and software security and implementing security checks prior to the Software Development Life Cycle.
As per the ‘Better Security and Business Outcomes with Security Performance Management’ study by BitSight, over 82% of stakeholders accepted that users perceive security as growing essential in making decisions for their enterprises. The damage connected with cybercrime is projected to hit $6 trillion annually by 2021, according to Cybersecurity Ventures.
In the year 2021, security practices are going to come more into play, and these are some reasons why:
• Regular pen test aids to build the enterprise’s trust with customers, 3rd-parties, and partners.
• Security tests offer you a comprehensive understanding of your enterprises’ weak points before hackers/ attackers do and assist in detecting areas susceptible to security or cyber threats.
• Cybersecurity tests guarantee that if any downtime happens, it is not as expensive and damaging as if you were not prepared.
Cybersecurity tests protect not just transactions (be it money or data), yet also secure their end-users. As cyber risks can easily happen at any moment, in any shape, cybersecurity testing will continue to be a buzz in the following year. Here are some crucial reasons why:
• Cybersecurity testing provides an in-depth knowledge of your enterprises’ weak spots before the hackers do.
• Security test assists to spot parts susceptible to cyber thefts or attacks.
• Regular penetration testing contribute to enterprises’ great reputations, and aids win immense trust between enterprises and third parties, their partners, and customers partners.
• Cybersecurity tests guarantee that if downtime happens, it is as costly and destructive as if you were not ready.
10. Significance of QAOps
QAOps is a better practice to bring QAs, operations, and developers’ altogether. Testing actions, together with CI/CD pipelines and QA engineers working parallel with the development team, are the two critical holders of QAOps.
Including QA in development and operations.
To achieve great quality and quick delivery, all testing and QA activities must be executed in the CI/CD pipeline. One of the better ways to integrate Quality Assurance in operations and development is for developers to commence by writing test cases whilst product designers and operation engineers identify UX/UI anomalies alongside the testing team. By implementing this, the developer and QA teams work mutually and gain a good understanding of the Quality Assurance process. This teamwork will then help to make the procedure of testing and development further efficient.
In a nutshell, QAOps is a rising trend that enables the automation of procedures between IT, software development, and Quality Assurance to deliver software rapidly and with superior quality. Hence, gradually more organizations are inclining towards DevOps, and this places QAOps on the go in 2021.
11. Incorporating Manual and Automation Testing
Automated thorough manual test effort is the perfect strategy to reveal the capacities of any skilled QA team. Combining these two efforts can increase productivity, save time with enhanced quality. There are definite aspects that automated tests can’t deal with. Currently, there has been a hike in automation and the requirement for automated QA engineers. With automation, the speed and efficiency of software testing augment considerably, but it can’t cover various aspects like design, user experience, and usability. The balance for both automated and manual testing in a software development procedure is the QA test’s future.
Why should we merge manual with automation tests?
The faster the QA team can detect the error, the less will be the time required to mitigate them, and therefore spending money on test resources is more valuable than spending it on errors after their release. It also illustrates that, all through testing, every technique, branch, situation, route, and choice has been well-tested so that glitches are discovered at the primary phase. If the bug is identified at the beginning, the expense gets minimized to fix it.
The code scope is managed at some stage in the test coverage; moreover, it scrutinizes the quality of every app’s function and minimizes the gaps amid demands as well as test instances. As the contribution of manual and automation tests is usually fixed by the app’s specifications, both methods should be used randomly to cover the code utmost.
Strength of Automated and Manual Testing
Automation testing has the benefit of consistency and speed; however, it lacks a user standpoint. This is wherein the manual test is better used, so it picks up where test automation leaves off. Both the techniques might be used to cover up distinct sections of the same traits or for coverage of entirely separate features. But, automation tests can only work and the scripts written for it, whereas manual tests are only as perfect as the QA engineers. Incorporating both these testing can result in a harmonious balance of usability, functionality, speed, minimized bugs, and an overall better user experience.
12. API and Service Test Automation
As per Gartner, ‘by the year 2021, at least 1/3rd of organizations will have deployed a multitudinous-experience development platform for supporting web, conversational, mobile, and augmented reality development.’ In the past decade, APIs haven’t just fuelled a new Digital Economy, however, also created a wings race of innovation that is forcing several enterprises to rethink how they build as well as bring new apps.
With the enhancement of microservice architecture on the web as well as software development, the usage of application programming interfaces (APIs) is mounting every day. APIs are being utilized in almost every component. Even the Client-Server development is at the peak, and the QA team must confirm these APIs are communicating perfectly with each other, plus functioning separately. To keep this procedure highly effective, automated testing on the Application Programming Interface and service level will rise as we move toward 2021.
13. Testing Centers for Quality
Enterprises face enormous challenges as they try to manage app quality while responding to additional demands from the business, counting inconsistent testing procedures across locations, geographies, and test groups, under-executing testing and QA functions, and sub-optimal consumption of resources, infrastructure, and tools. In return, several big giant companies are looking to Centers for Quality models with devoted teams determined on standardizing deliverable implementation models to make sure the quality of significant business systems and processes. Testing Center of Quality is a model for a centralized test platform that offers standardized test procedures and optimum use of resources for quality and test reasons.
The testing center for Quality has test teams devoted to building up a reusable tests framework and standards for enterprises to follow whilst developing. In the long term, this aid in building superior-quality software and enhances the overall workflow of the software development procedure. Executing these centers will also reduce test time without sacrificing the quality of the product’s performance, usability, and functionality. It will also offer effective automated tests and make flexible standards in QA practices to be executed in upcoming projects to come.
Some of the other rewards of Testing Centers for Quality models being in are more agility to Quality Assurance and helps to set up a continuous improvement procedure driven by metrics.
14. Infrastructure as Code (IaC)
Cloud-based solutions are being utilized by several enterprises, mainly IT firms, in enormous numbers, to attain cost-effectiveness, scalability, and flexibility. The increasing use of cloud and virtualization has modified the way servers were used. It has simplified the bottleneck that was a problem in the past to allocate servers and configure them. The leading-edge infrastructure management technology has modernized the procedure of managing the architecture. The use of varied tools such as Terraform, Kubernetes, Docker, etc., is on the hit and will continue to dominate in 2021.
As the name proposed, infrastructure as code (IaC) is mainly the concept to manage your operations environment in a similar method, you do apps or other code for normal release. In spite of manually making configuration modifications or making use of one-off scripts to make infrastructure changes, the operations infrastructure is controlled instead using the similar structures and rules that govern code development chiefly, while new server instances are turned up. It means that the core DevOps best practices such as virtualized tests, continuous monitoring, and version control are applied to the underlying code that governs the design and management of your infrastructure. In simple words, the infrastructure is treated the exact similar way that any other code would be.
The use of advanced coding systems such as Puppet or Ansible is designed to make infrastructure as code environments available to any person with fundamental knowledge of modern coding structures and techniques.
Four best practices of IaC:
Apply test to infrastructure in the form of integration testing, functional testing, and unit testing.
Manage infrastructure through source control, therefore giving a thorough audit trail for alterations.
Allow incorporation around infrastructure configuration and arrangement, particularly between dev and ops.
Evade written documentation, as the code itself will document the machine‘s state. This is critically powerful as it means, for the first time, that documentation concerning infrastructure is always updated.
IaC Makes DevOps Possible
In easy words terms, Infrastructure as code is a framework that takes proven coding methods, practices and extends them to your infrastructure straight, efficiently blurring the line between what is an app and what is the setting. In a nutshell, this is the similar thing DevOps is doing with the personnel in charge of these two worlds, melding operations and developers staff into a single unit with a portmanteau of a name.
Infrastructure as Code Benefits
Manual procedures result in errors, period. Humans are not always perfect. Communication is tough, and we are generally pretty bad at it. Even sometimes, manual infrastructure management would cause discrepancies, regardless of how hard we try. However, Infrastructure as Code solves that issue by having the configuration files themselves be the only source of truth. In this way, one guarantees similar configurations will be deployed again and again, with zero discrepancies.
The main benefit Infrastructure as code offers is speed. IaC allows you to rapidly set up your entire infrastructure simply by running a script. One could easily do that for each environment, from development to production, exceeding all the way through staging, Quality Assurance, and more. Infrastructure as Code can make the whole software development lifecycle highly effective.
This one is easy and fast. As you can version Infrastructure as code configuration files similar to any source code file, you have complete traceability of the modifications every configuration suffered. No more presumption games anywhere.
• Lower Cost
The major benefit of Infrastructure as Code is, undoubtedly, lowering the infrastructure management expense. By employing cloud along with Infrastructure as Code (IaC), one could dramatically reduce the expense.
• Effectiveness During the Entire Software Development Cycle
By employing IaC, you can set up your infrastructure architectures in several phases. That makes the entire software development life cycle more competent, increasing the team’s efficiency to new heights.
15. Chatbot Tests
With the coronavirus epidemic, chatbots have become well-liked within the healthcare industry by offering remote support to patients plus several other sectors. Due to global lockdown for months in a row, several companies implemented chatbots. Even chatbots provided 24×7 support to millions of retail stores, financial organizations, brands, etc. ChatBots will continue to conquer the globe as a part of RAP (robotic process automation). Bots allow reducing costs on support while giving a better user experience. The smooth functioning of chatbot necessitates careful testing.
Three Most Popular Chatbot Testing Tools to Consider:
The open-source guide provides around 120 questions to assess the user experience that your chatbot brings. It generally operates at 3 levels:
• expected scenarios.
• possible chatbot test scenarios.
• nearly impossible scenarios.
The good thing about this chatbot test tool is that it incorporates seamlessly with key platforms such as Slack, Telegram, Facebook Messenger, and WeChat. Make use of it to discover any errors in the conversational flow of your bot, in the user experience that it offers.
Right from conversational flow to usability to the delivered user experience, this customized service allows you to test each main aspect of your chatbot.
What Are the Top Software Testing Tools Available at This Time?
As per reviews from software test teams, the testing community seeks end-to-end, cross-platform testing solutions and robust test automation capacities. Here are some of them:
Katalon Studio: It is an automated tool for mobile, Web, API, and desktop app testing.
Selenium: It is a household name in automated testing for several years.
SoapUI: It is a headless functional test tool particularly designed for API tests.
UFT One: A paid tool and one of the best tool options for mobile, web, desktop, and RPA app tests.
TestComplete: It is an Artificial Intelligence-powered test automation tool for mobile, desktop, and web testing.
Other notably powerful and robust tools include Ranorex, Apache JMeter, Postman, Cucumber, Tricentis Tosca, Appium, Telerik Test Studio, and Worksoft. All are valuable to check out if nothing of the choices in the list above seems perfect for anyone.
Future Forecast of Software Testing
In order to leverage a product-market fit, several companies pay their complete attention to quality and depend on professionals working for the software testing company. The solutions provided by such companies help you find resources and skilled software testers or QA engineers that are mature in the matter of accomplishment and applied technologies. It is estimated that there will be an expansion in independent testing in the upcoming decades. Concentrating on security and automated tests could also be a wise decision. With a transformative influence on your business in 2021, it could be better to refocus Quality Assurance on user experience and build it on the DevOps and agile best practices. In order to move products to market rapidly, consulting independent software testing companies would be better to address concerns professionally.
These are the most recent trends in testing that are exceedingly useful for organizations and businesses. No matter whether you are a testing company or a QA professional, you need to continually brush up with these emerging software testing trends to stay ahead in the competitive and ever-changing industry.
With an immense number of companies and entities climbing onto the digital bandwagon, cybersecurity considerations have come up as limelight. Besides, new technologies such as Big Data, IoT, and Artificial Intelligence/Machine Learning are gradually more making inroads into our everyday lives, the threats related to cybercrime are mounting as well. Additionally, the usage of mobile and web apps in transacting financial information has put the complete digital stuff exposed to cybersecurity breaches.
The inherent risks and vulnerabilities found in such apps can be exploited by attackers or cybercriminals to draw off crucial information data counting money. Internationally, cyber-security breaches have caused a yearly loss of USD 20.38 million in 2019 (Source: Statista). Plus, cybercrime has led to a 0.80 percent loss of the entire world’s Gross domestic product, which sums up to approx. USD 2.1 trillion in the year 2019 alone (Source: Cybriant.com).
Statista Report 2018 “Security Threats at All-Time High”. The no. of security threats or vulnerabilities in all kinds of Information Technology software is at an all-time high.
Even the spiraling pandemic has introduced a distressing impact on several enterprises and companies worldwide, the majority of companies arbitrarily attempted or moved their business sections to the untouched or unaffected digital space. Most security funds were, yet, also battered as a collateral outcome of the complete economic downturn. The shrinking budgets mainly exacerbated traumatic digital transformation by gross disregard of privacy and cybersecurity components of the subtle process.
To stem the rot and preempt adverse penalties of cyberthreat or crime, like losing client trust and brand repute, cybersecurity testing should be made compulsory. Cybersecurity expense is nonetheless forecasted to rebound and hit again in the year 2021, giving relief for exhausted CISOs, and their shattered IT, cybersecurity teams. Meanwhile, I would like to acquaint you with a series of best cybersecurity tools that can make a palpable divergence for your overall security program and 2021 budget plans.
What Is Penetration Testing?
The penetration test is a kind of Security testing that is carried out to assess the security of the system (software, hardware, information system, or networks environment). The main objective of this type of testing is to scrutinize all the security risks or vulnerabilities that are found in an app by assessing the system’s security with malevolent techniques and to safeguard the data from the hackers and manage the system’s functionality. Penetration testing is a kind of Non-functional test which intends to make official attempts to breach the system’s security. It is also called a Pen Test or Pen Testing and the QA engineer or tester who performs this testing is considered as a pen tester aka ethical hacker.
What Are the Best Cyber Security Tools for 2021?
Any app security testing method shall require the conduct of a functional test. This way, several security issues, and vulnerabilities can be detected, which if not rectified in time can result in hacking. There are a plethora of paid and open source testing tools available in the market. Let’s discuss the top 10 cybersecurity testing tools to look out for in 2021:
NMap is a short form of Network Mapper. NMap is an open-source and free security scanning tool for security auditing and network exploration. It works on Windows, Linux, HP-UX, Solaris, BSD variants (comprising Mac OS), AmigaOS. NMap is used to detect what hosts are accessible on the network, what versions and OSs they are running, what services those hosts are providing, what kind of firewalls/ packet filters are in use etc., Several network and systems administrators find it beneficial for regular jobs like check for open ports, maintaining service upgrade schedules, network inventory, and monitoring service or host uptime. It comes with both GUI interfaces and command line
Determines hosts on a network
It is used to determine network inventory, network mapping, maintenance, and asset management
Produces traffic to hosts on a network, response time measurement, and response analysis
Used to recognizes open ports on target hosts in the arrangement for auditing
To search and exploit risks as well as vulnerabilities in a network
It is one of the best tools and freely accessible open-source pen-testing tools. Generally, it is one of the network protocol analyzers, it allows you to capture and coordinatively browse the traffic running on a system network. It runs on Linux, Windows, Unix, Solaris, Mac OS, NetBSD, FreeBSD, and several others. Wireshark can be extensively used by educators, security experts, network professionals, and developers. The information that is recovered through the Wireshark software testing tool can be viewed through a Graphical User Interface or the TTY-mode TShark utility.
Rich VoIP analysis
Live capture and offline scrutiny
In-depth examination of hundreds of protocols
Runs on UNIX, Linux, Windows, Solaris, macOS, NetBSD, FreeBSD, & various others
Captured system or network data can be browsed through a GUI, or through the TTY-mode TShark utility
Read/write several variant capture file formats
Captured files compressed via gzip can be de-compressed concurrently
Coloring rules can be applied to the packet list for intuitive and rapid analysis
Live data can be read from Blue-tooth, PPP/HDLC, internet, ATM, Token Ring, USB, etc.,
Outcome can be exported to PostScript, CSV, XML, or plain text
It is a computer security project that offers the user vital information about security risks or vulnerabilities. This framework is an open-source pen test and development platform that offers you access to the recent exploit code for several apps, platforms, and operating systems. Some of the jobs that can be attained in Metasploit from a pen test perspective comprise vulnerability scanning, listening, and exploiting known vulnerabilities, project reporting, and evidence collection. It has a command-line and Graphical User Interface clickable interface that works on Linux, Windows, as well as Apple Mac OS. Metasploit is a commercial tool but it comes with an open-source limited trial.
It has a command-line and GUI interface
Works on Windows, Linux, & Mac OS X
Vulnerability scanner import
Metasploit community edition is offered to the InfoSec community without charge
This commercial security test tool is a web app security scanner. Netsparker is a deadly accurate, automatic, and simple to use web app security scanner. This amazing tool is mainly used to identify security risks automatically like Cross-Site Scripting (XSS) and SQL injection in web services, web apps, and websites. Its proof-based Scanning technology does not simply report risks; it also generates a Proof of Concept to confirm they aren’t false positives. Therefore, there is no point in wasting your time by verifying the detect vulnerabilities manually after a scan is ended.
Advanced web scanning
HTTP request builder
Web services scanning
Proof-centric scanning technology for dead-accurate threats finding and scan outcomes
Full HTML5 support
Automated identification of custom 404 error pages
Anti-Cross-site Request Forgery (CSRF) token support
It is a completely automatic web vulnerability scanner that identifies and reports on over 4500 web app vulnerabilities counting all variants of XSS XXE, SSRF, Host Header Injection, and SQL Injection. Acunetix smartly detects around 4500 web vulnerabilities. Acunetix is a commercial tool. Its DeepScan Crawler scans AJAX-heavy client-side SPAs and HTML5 websites. It enables users to export detected vulnerabilities to problem trackers like GitHub, Atlassian JIRA, Microsoft TFS (Team Foundation Server). It is obtainable on Linux, Windows, and Online.
A high detection rate of risks and vulnerabilities with lower false positives
Integrated vulnerability management — organize and control risks
Deeply crawl and scrutinize — automatic scans all websites
Integration with popular WAFs and Issue trackers like GitHub, JIRA, TFS
Open-source network security scanning and Manual test tools
It is a vulnerability assessment solution for security practitioners and it is formed and maintained by a company called Tenable Network Security. Nessus aids in detecting and fixing vulnerabilities like software flaws, malware, missing patches, and misconfigurations across a variety of OSs, apps, and devices. It supports Windows, Linux, Mac, Solaris, etc. It specializes in IPs scan, website scanning, compliance checks, Sensitive data searches, etc., and assists to detect the ‘weak-spots’.
Mobile device audits
Reports can be simply tailored to sort by host or vulnerability, generate an executive summary, or compare scanning outcomes to highlight alterations
Detect vulnerabilities that enable a remote attacker to access confidential data from the system
Identifies both the remote faults of the hosts that are on a network and their local flaws and missing patches as well
It is a Web Application Attack and Audit Framework. W3af is a free tool. W3af secures web apps by searching and exploiting all web app vulnerabilities. It determines 200 or more vulnerabilities and controls your overall risk exposure on the website. It detects all sorts of vulnerabilities such as Cross-Site Scripting (XSS), SQL injection, unhandled application errors, Guessable Credentials, and PHP misconfigurations. It has both a console and graphical UI. It works on Mac, Linux, and Windows OS.
Assimilation of web and proxy servers into the code
Injecting payloads into roughly every section of the HTTP request
Zed Attack Proxy is a free and open-source security testing tool, developed by OWASP. Popularly known as ZAP, Supported by Unix/Linux, Windows, and Mac OS, ZAP allows you to find a set of security risks and vulnerabilities in web apps, even at the time of the development and testing phase. This tool is simple to use, even if you are a novice in pen-testing.
It is also essentially a scanner (with a restricted “intruder” tool), even though several security test experts swear that penetration test without this tool is unimaginable. It isn’t free, yet very lucrative. It generally works wonders with crawling content and functionality, intercepting proxy, web app scanning, etc. One can use this on Mac OS X, Windows, and Linux environments.
It is one of the best open-source penetration testing tools. The aim of Sqlninja is to exploit SQL injection threats and vulnerabilities on a web app. This automated testing tool utilizes Microsoft SQL Server as a back-end. Sqlninja has a command-line interface. Sqlninja works on Linux, as well as Apple Mac OS X. It comes with a slew of descriptive features, counting remote commands, DB fingerprinting, and its detection engine.
Direct and reverse shell, both for UDP and TCP
Fingerprinting of the remote SQL Server
Formation of a custom XP cmdshell when the original one has been disabled
Withdrawal of data from the remote Database
Operating System privilege escalation on the remote database server
Reverse scan to seek a port that can be utilized for a reverse shell
These are the top cybersecurity testing tools that will give you security for your personal data, mitigate the rates of data breaches, as well as stolen hardware. Other advantages of these tools count tighter security and greater privacy. These must-have security tools will help you evade cyberattacks and secure your IT infrastructure. Lastly, such security software requires up-gradation and maintenance to constantly have top-notch security.
The Certified Ethical Hacker (CEH v11 Training) and certification program is the most trusted certification that updates your know-how of main security essentials and fundamentals. CEH V11 Certification Course introduced by EC-Council is the globally accepted and treasured security training and certification course worldwide. This most demanding security training demonstrates your capabilities to detect the vulnerabilities and risks in the enterprise’s network infrastructure, and adequately prepare you to enhance your blue team skills and aids to deal with cyber-attacks successfully and efficiently.
The Certified Ethical Hacker (CEH v11 Training) Training is the most popular and 2nd course in the newest VAPT (Vulnerability Assessment and Penetration Testing) Track. EC-Council in the newest version has added in-depth concepts and topics considering the current improvements in the cyber-security field. The training course trained you with the know-how of the most recent commercial-grade attacking or hacking methodologies, practices, and tools used by real-life attackers and information security professionalsto ethically hack any company.
Even since the launch of CEH in the year 2003, it is considered as a benchmark within the biggest community of cyber-security experts in the industry called the information security community. CEH v11 continues to launch the most advanced hacking tools and the hacking techniques and exploits used by information security professionals and malicious hackers today. The 5 Stages of Ethical Hacking and the original objective of CEH remain official and relevant today: “To beat a hacker, you need to think like a hacker.”
You would scrutinize, test, hack, and protect your systems. You will be taught the 5 stages of ethical hacking and the manners to hit your target and thrive at breaking in each time! The 5 crucial phases comprise Reconnaissance, Acquiring Access, Enumeration, Manage Access, & Covering your tracks.