How can AI help in making Software Testing successful?

The increased emphasis on the quality of software and accelerated time to market has made software development and testing a complex affair. Add to this the complexities arising out of API interaction and leveraging of legacy systems and you end up mulling about a new and innovative way to manage the whole canvas of software testing. Moreover, studies have shown that the latest software testing approach involving a greater testing of mobile and hybrid applications has shrunk the actual time for development.

The breakneck speed of the launching of new products or services has reduced the scope for keeping the development team updated of the impending software issues. This in turn has made the developers and testers understand and appreciate the need for a smarter and efficient software testing approach. Since artificial intelligence and machine learning have become the new technology juggernauts, it is about time the software testing companies also adopt these to bring efficiency, scale, and speed to their testing efforts.

Before extolling the virtues of artificial intelligence and machine learning, let us first understand why the traditional software testing methodology has come a cropper in meeting these challenges.

Changing user preferences: The traditional software testing approach is more about identifying and fixing the mismatch in a test case when an input does not behave as per expectations. In such a case, the tester should typically run a checklist to mimic the steps of the users. Such an approach used to suffice when the amount of data was relatively manageable. However, with the development of new applications having multiple interfaces with devices, platforms, networks, and frameworks, the amount of data generated has become humongous. Consequently, the testing of such big data with the traditional method of QA has become difficult and calls for a software testing approach involving new technologies such as artificial intelligence, machine learning, and predictive analysis.

Shortcomings of manual testing: Humans are not capable of showing constant attention to the job at hand. They are prone to making errors and overlooking the bugs in the system. Besides, any repetitive nature of testing would leave the human element exhausted and error prone. This is where test automation with machine fed intelligence can produce accurate test results. So, no matter how big the data is or how frequent or repetitive the tests are, AI and predictive analysis can be of help.

Greater automation: The manual way of testing has its glaring restrictions. For example, humans cannot work round the clock and have a limited attention span. On the other hand, machine aided testing can be done on a consistent basis irrespective of the time period. This approach suits the requirements of Agile and DevOps especially related to continuous testing, integration, and delivery. Thus, not only the testing gets accurate and time saving, it enhances the user experience and ROI as well.

Let us find out how software testing with AI can be made possible

Predict customer needs with machine learning & predictive analytics: The changing market dynamics and increased competition have necessitated the need to predict what the customers want. So, instead of the customers reaching you, it would be better if you reach the customers beforehand with their choicest products/services. This would mean predicting their shopping needs by analyzing past consumer behaviour. The behaviour would bring to light the shopping patterns and give you an insight into the kind of products or features the customers would seek.

Streamline the testing process: Frequent product releases and updates can put a lot of strain on the testing team and processes. The machine induced testing a la AI can register changed controls and updates much more efficiently than humans. Importantly, the vast range of dependencies with hundreds of input combinations can be validated accurately and consistently with AI.

Types of tests and data driven decisions: While planning for QA, testers are often at their wit’s end about finalizing the test processes. This is where AI with its user behaviour analysis can be of help. In fact, AI can ascribe a risk preference while analyzing the user behaviour. It can monitor, evaluate, and categorize the same, and determine the kind of tests to be conducted. Testers, on their part, can automate redundant test cases and take data driven decisions. When time is a critical factor in testing, it is better to focus on crucial test cases and give them a greater coverage by leveraging risk-based automation.

Saving time and cost: When the help of AI is taken in terms of creating, modifying, analyzing, and executing test cases, testers can do away with manually updating them. They can rather identify controls and defects much more effectively. Also, when the source code undergoes modification due to repeated software testing, validating them each time manually is time consuming and costly. Whereas an AI based test automation can be executed repeatedly at a faster rate leading to cost savings.

Use of AI based tools: AI or machine learning based test automation tools such as Functionize, Test.ai, Testim.io, Appvance and others can create, execute, and maintain various types of testing thereby increasing the stability of test suites.

Conclusion

Artificial Intelligence and machine learning have found their way into the SDLC leading to a better authoring and executing of test cases. Notwithstanding the initial investment in implementing an AI based test automation system, the results in terms of better products that suit the customer needs, can save costs and improve your brand equity.

Diya works for Cigniti Technologies, Global Leaders in Independent Software Testing Services Company to be appraised at Cmmi-Svc v1.3, Maturity Level 5, and is also Iso 9001:2015 & Iso 27001:2013 certified.