#ai testing framework

To meet the digital transformation goals where DevOps and Continuous Delivery are the key paradigms, businesses have their task cut out. The growing competition has led companies to innovate fast and deliver products and services that are free of glitches. This means assessing risks on a real time basis across the SDLC by leveraging advanced technologies such as Artificial Intelligence and Machine Learning. Also, the availability of big data and computing power has led the IT thinktanks in business to consider AI as a strategic investment.

To be a digital disruptor in your domain and hold a competitive edge, there should be adroit management of the customer experience. This can, however, be a difficult proposition given the broad spectrum of software testing. Today, a software application or system interacts with myriad digital touchpoints through APIs in a nonlinear fashion. Also, the digital environment is witnessing rapid changes driven by technologies such as IoT, quantum computing, and robotics among others. These developments have created a challenge of sorts for digital testing necessitating the use of machine-based intelligence.

How can Artificial Intelligence help QA?

With the complexity of applications, interfaces, and digital environment increasing, AI application testing can be used to conduct a plethora of tasks such as:

Building and optimizing test cases

Selecting the right test cases for automation

Carrying out predictive analysis

Speeding up processes conducting routine analysis

Determining outcomes beforehand for complex test cases

Is AI in software testing going to be the new normal in QA?

In traditional QA, the modus operandi involves looking for an expected output after putting in the data manually. Even when test automation is introduced, some of the tasks like input of data are driven manually. However, with AI application testing, the software is made to understand the input data and predict the outcome accurately. The best part of involving AI testing services is the felicity with which the machine or tool carries out testing in various ways. Moreover, should there be any code or UI change, AI can direct the testing tools to handle it in a seamless manner. So, be it creating test codes, anticipating outcomes, handling code changes, or predicting glitches in thousand lines of codes, AI can deliver the goods with significant time and cost savings.

5 reasons how AI can significantly enhance QA

#1. Automating the QA architecture: The complex scenario of QA testing makes the identification of test processes a difficult exercise. Since not all processes can be automated, it is a challenge to choose the ones that need to undergo automation. Also, every test case comes with numerous dependencies, especially between attributes and instances. Handling such dependencies with information being added, deleted or modified due to a change in business logic can be a nightmare. A robust AI testing framework, on the other hand, can build appropriate test cases as per the defined rules serving twin purposes – pre-empting the creation of meaningless test cases and creating test cases to plug gaps in the risk coverage area.

#2. Optimization risk coverage: QA experts are often clueless about the risks associated with various processes and their touchpoints. To achieve the best results in testing, the QA exercise should make use of AI testing services. The best results can cover outcomes such as identifying the maximum number of defects, minimizing the duration of testing, reducing the number of test cases and resources, and increasing the risk coverage area.

#3. Identification and resolving test failure cases: Test automation can often lead to an increase in the number of failed test cases, especially for regression tests. This can happen when the rate of test automation, the frequency of testing, and system complexity show an increase. Other reasons showing a test case failure can be due to the unavailability or slowdown of a third-party API or any change in the test application breaking a business-critical functionality etc. Diagnosing the root cause of such test case failures can be time consuming unless AI is taken into consideration. Using AI in software testing can quickly identify and eliminate the reasons for such failures.

#4. Reducing the burden on QA experts: The Agile and DevOps framework entails creating a sprint wherein QA experts identify glitches post the development of a code. However, artificial intelligence app testing can be leveraged even by the development team to do a preliminary test screening. This can reduce the overall turnaround time as developers are able to identify and eliminate glitches in the codes beforehand. Moreover, they can even modify the codes to pre-empt bugs from sneaking in.

#5. Test simulation: AI testing can simulate any number of test users to validate various functionalities associated with an application. This can help in testing each and every test case and its interface with various digital touchpoints leading to better identification of glitches.

Conclusion

AI can influence new areas of testing such as predictive analysis, intelligent automation, and design thinking leading to quality engineering. It can help enhance business agility and competitiveness.

Software applications have grown in complexity thanks to their interfacing with myriad devices, legacy systems, operating systems, frameworks, networks, browsers, and cloud platforms. Moreover, the complexity often grows in a non-linear fashion making the job of testers a challenging exercise. Since the proper functioning of these applications has a bearing on achieving customer satisfaction, not to speak of the business staying competitive, the quality needs to be top notch.

The changing dynamics of evolving technologies and customer choices has led businesses to adopt the methodology of Agile-DevOps. The objectives of Agile-DevOps in terms of ensuring Continuous Integration and Delivery have led software testers to embrace test automation in a big way.

However, test automation can have its pitfalls too as follows:

The inability to identify the processes for test automation.

The difficulty in writing automated test scripts for software testing.

Selecting the test tool compatible with the specific test.

To get around the issue, artificial intelligence based software testing should be used. The technology can help in determining risks and anomalies in the processes, redundancies, and predicting bottlenecks. It helps testers to keep away from the requirements of updating test cases continually and identify the defect trail and associated program components more effectively. Moreover, AI application testing can dig deep and analyze the outcomes of exploratory testing by the users. The analysis can help developers to sort out glitches even before the code is delivered to the testing team in the sprint. AI application testing can analyse past test records and identify patterns leading to the prediction of glitches in the application. And should the development team be informed of the nature and type of glitches that have got into the application, proper correctives can be built into the coding process.

AI test automation can come up with multiple variations of a test and run more number of tests – without the supervision of a human. AI application testing can make changes to the code and UI, which otherwise are carried out by QA testers. It can automatically create virtual test variables and users in required numbers to test each and every component of an application. Thus, it can do away with the time consuming regression testing and generate outcomes for testers to analyse. The AI led testing can make test automation more efficient not to speak of doing away with cost and time constraints.

The benefits of incorporating AI in software testing

Better accuracy: The advantages of using test automation in identifying hidden glitches in the system can be multiplied with AI. Apart from selecting the right test tool to writing automated test scripts, AI led test automation can perform QA testing more effectively. It can relieve the QA experts from selecting the right tool or supervising the conduct of test. AI can provide relevant test records for the test team to analyze and let both the test and development teams with relevant data to improve the processes even further.

Simulation of virtual users: A software with multiple features and functionalities should ideally be tested by a large number of users. This is to pre-empt any possible glitch, which might not manifest in each and every test. The artificial intelligence app testing exercise can generate hundreds or thousands of virtual test users thereby validating each and every test variable repeatedly. The simulation can help cut down the cost and time of testing.

Impact analysis: The AI app testing services can identify the impact of glitches in applications practically bypassing the involvement of subject matter experts. These can easily identify the relationships between various entities of an application should there be glitches. AI can help with an effective triage of glitches leading the development team to correct anomalies in its processes.

Predicting glitches: AI can analyze huge amounts of test data gleaned out of defect logs and social data to identify segments or modules containing glitches. It can help the test team with prioritising the regression test suite and shifting left for high risk cases.

Traceability: Often the writing of specific test cases are missed out during the software testing process. These test cases remain untraceable until the end of the SDLC leading to expensive rework later on. AI-led software testing can identify the missing test cases effectively and pre-empt their impact on actual testing.

Conclusion

AI can effectively mine user feedback and develop insights into the needs of the customers. It can prioritize the critical areas for testing in order to identify the rigorous issues plaguing the end user experience.

The digital transformation initiatives of enterprises have become comprehensive and complex in their scope and challenges. These initiatives entail the development and delivery of myriad software applications, which in turn leverage a host of digital elements. These elements include API’s, legacy systems, platforms, frameworks, cloud, cutting edge devices, operating systems, browsers, and networks. The complexities and challenges brought about by the interaction of applications and digital elements (as mentioned earlier) have witnessed a paradigm shift in the Quality Assurance process. To ensure deliverables such as quality and cost savings within small turnarounds, the leveraging of machine based intelligence has become important. Artificial intelligence, apart from improving the quality and scope of test automation, helps in analyzing huge chunks of data to draw meaningful inferences. AI application testing is poised to do away with traditional manual testing processes and help stakeholders to implement the Agile-DevOps model in a better way.

The artificial intelligence testing services assess and analyze software applications under specific parameters/conditions to identify the risks and overall threshold before their eventual delivery. AI testing paves the way for developers, testers and members of the operations unit to be convinced of the quality of software as well as understand the specific software attributes that would appeal to the end customers. AI testing helps the QA teams to impart quick and precise feedback to the development teams. This is of utmost importance given the speed of software launches and the mindboggling array of digital elements the software needs to have an interface with. The QA requirements of today have shifted to smart testing from the traditional waterfall model of testing. Furthermore, the pace of new software releases notwithstanding, the frequent updates too are needed to be factored in by the QA team. This is where leveraging AI application testing becomes essential.

Let us delve into five reasons as to why artificial intelligence has become crucial to enhance software quality.

Why use artificial intelligence app testing?

#1. Better accuracy: A proper QA exercise includes the validation of each and every feature and functionality of the software. This is not always possible in the traditional waterfall model of manual testing given the factor of human fatigue in testing repetitive and complex processes. Even the much vaunted test automation might come a cropper if the QA team is not able to create proper test scripts for specific processes. However, AI application testing can analyze the testing requirements and create suitable automated test scripts. Also, the AI testing tools can generate meaningful data for the QA team to take relevant inferences about enhancing the quality of software. As testers are freed from carrying out mundane tests, they can utilize the time to create better test automation suites.

#2. Simulated testing: Given the preponderance of test variables interfacing with myriad digital elements, it is virtually impossible to validate them by any QA department. However, AI app testing services can simulate any number of virtual users (even running into thousands) who can interact with the software application. In doing so, these set of virtual users can validate the features and functionalities of the software application, especially when the latter interfaces with sundry digital elements. The virtualization process checks each and every test variable and identifies the glitches lying therein. Thus, the quality of software gets enhanced significantly in tune with the business requisites leading to customer satisfaction.

#3. Easy testing process: The foremost challenge for any QA tester is to write automated test scripts that examine a vast amount of data. This can delay the testing process thereby impacting the overall turnaround time – a critical lapse that can have an adverse impact on business. Remember, customers are generally attracted to new products, and if the same turn out to be innovative, qualitatively superior, and feature rich, the business delivering such products can reap rich dividends. However, such lapses can be easily mitigated or eliminated by leveraging artificial intelligence app testing. The powerful algorithms of AI can create sophisticated test scripts that can analyze and sift through copious amount of data. Moreover, AI can analyze past test records and evaluate the entire SDLC to suggest the areas of testing and predict glitches. This can significantly increase QA efficiency and save time.

#4. Helps developers: The development teams are dependent on the QA teams to identify glitches that may have been incorporated into the software. This has a bearing on the overall turnaround time. What if, the development teams conduct some sort of initial screening of the codes before handing the latter to the QA teams? This can help the development teams to remove the gaps or bottlenecks that have crept into the codes. This becomes possible when an AI testing framework is considered as a part of DevOps. The development teams can leverage the AI testing tools to conduct an initial screening exercise. Consequent to this approach, the efficiency of the QA process improves and crucial time/cost is saved, which otherwise would have gone into rework. AI can detect the source of bugs in the software, which can help the development teams to carry out suitable modifications in the codes thereby pre-empting the entry of bugs.

#5. Improves the test coverage area: Traditional QA leaves a lot to be desired when it comes to the scope of testing. AI, on the other hand, can look into areas such as memory, database, internal file contents, and program states to test if the software is delivering outcomes that are consistent with the expected ones.

Conclusion

The AI app testing services are increasingly leveraged by enterprises to turn their QA processes precise, elaborate, quick, and cost effective. These services analyze and offer test outcomes that improve quality, streamline processes, quicken the pace of delivery, and achieve ROI.

In the traditional setup of executing software quality assurance, a lot of data and information are often not considered for further analysis. This shortcoming bears a negative impact on the QA efficiency such that the stability of application can be called to question. The data and information appearing in the form of test results, defect logs, project documentation, and production incidents are voluminous and complex to decipher and analyze. Even test automation as a part of the Agile or DevOps set up cannot be of help here, for it only executes pre-written test scripts iteratively to generate outcomes that may or may not meet the test objectives.

Where typical software test assurance can fail, artificial intelligence and machine learning can chip in to ensure enhanced QA efficiencies and better identification of glitches, defect hotspots, and failure patterns. In fact, the AI-based algorithms can analyze the wealth of information gleaned out of traditional QA software testing and predict a host of possibilities. These can range from offering insights into the stability of application concerned and defect hotspots to enhancing the decision making capability, predicting future defect areas, and incorporating quality in the early part of the SDLC.

How can AI led QA software testing be of help?

Analyze the historical data and predict the key test parameters for future tests

Identify duplicate test cases and optimize the test suite

Execute test cases automatically by identifying the hotspots

Achieve a better test coverage

Analyze social media data from customers and offer a comprehensive feedback report detailing the pain areas of customers

Improve regression testing by identifying the high risk glitch prone areas of an application

Achieve the development and delivery of a qualitatively superior application

Deploy applications faster leading to a rich user experience

Implement with ease on client’s technology platforms

There are a number of AI test automation tools that can be used by the quality assurance software testing services. Some of these are as follows:

·         Testim.io: Specializing in functional and usability testing, Testim.io uses machine learning to execute QA – from authoring to maintenance. The QA testers can use HTML and JavaScript to write algorithms, which can then go on to enhance the quality of software being tested. The tool also helps to increase the stability of test suites.

·         Appvance: This particular AI-based tool generates test suites to check the usability of an application based on past user behaviour. Since this tool checks the ultimate end user experience, it can play an important role in QA testing services.

·         Functionize: As evident from its name, the tool based on machine learning can analyze and create multiple tests to execute functional testing.

·         Test.ai: Useful in carrying out regression testing, the tool identifies the performance metrics and monitors the performance of a mobile application.

Criteria to choose the right tool for AI and software quality assurance testing

·         Stability and CI/CD integration: In the DevOps software development and testing methodology, there is a need to execute Continuous Improvement/Continuous Delivery of software to achieve user satisfaction. The CI/CD integration also helps in improving the quality of an application. However, predicting the need and areas to be covered for CI/CD integration require an AI-based tool for quicker execution.

·         Actionable intelligence: The AI tool can analyze a wealth of data and information produced from test reports to generate actionable intelligence for the testers to execute. This dynamic and easily accessible intelligence can come in handy while validating a software in areas such as regression testing, performance, security, functional, and usability.

·         Easy integration with software development and test tools: The AI tool to be selected for QA testing should be compliant with the software used by testers and developers. The test team should not be forced to learn a new language to operate the AI tool, which otherwise would lead to a delay in test execution. Moreover, the AI tool should not induce a process change while authoring or executing the QA test, for it would then require the test teams to be retrained.

Conclusion

Implementing AI in QA testing can produce better test outcomes at a lesser cost. AI can analyze voluminous test reports and predict the hotspots for glitches. In future, the role of artificial intelligence will transcend testing and cover the development and delivery of software applications.