Why should AI and machine learning be used in software testing?

Artificial intelligence

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.

Diya works for Cigniti Technologies, which is the world’s first Independent Software Testing Company to be appraised at CMMI-SVC v1.3, Maturity Level 5, and is also ISO 9001:2015 & ISO 27001:2013 certified.

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