In this day and age, when technological advancements are happening across various sectors, we can see massive transformations in the working of an industry. The same can be said about the Software Testing industry, all thanks to the introduction of Artificial Intelligence (AI).
It has increased the speed of execution of test cases and decreased the possibility of defects cropping up, thanks to Machine Learning and Analytics. However, one thing that still remains uncertain with AI in testing is the ROI it brings to the table.
Here, we give you a brief understanding of how you can manage the ROI and the possible benefits of incorporating AI the right way in software testing processes.
How to manage AI ROI?
To start with, managing the ROI of AI depends on the industry where it is being used. For instance, if predictive analysis and metric applications, which are based on AI, are used in the manufacturing industry, then it can cut the maintenance cost substantially, which results in good ROI.
However, when it comes to collecting reliable metrics to improve the customer interaction during online shopping, it can be a little more challenging as it involves the heavy use of AI and ML without any guarantee of a positive ROI. This is the exact reason why investors should have a clear understanding of the possible returns from an investment done in the field of AI, while developing and testing an application.
Bottom line: You need to identify the areas where non-AI solutions are being currently used that can see a major shift in quality by introducing AI into it so that you do not incur any loss.
Applications of AI in Testing
Improves the quality of the software
One of the definite benefits of using AI in the testing phase is that it improves the overall quality of the software. It can be used to automate tests, generate test cases and eradicate the use of repetitive test cases. It also helps you track certain features of software and see how it interacts with the users, with the help of ML. This in turn, helps the software to make quick real-time decisions and hence improve the user experience by manifold.
Automates test designs
AI can help in creating and automating test designs for a particular software in the near future, with respect to the user requirements. In practice, AI should be able to understand the requirements and their context of use and then further analyse the risk contribution of a requirement in the overall scheme of things, in order to design a perfect test case.
Once the AI has gathered enough learnings, it can link them with the technical aspects of the software, which will help the testers derive new test cases with respect to new data. AI has the potential to derive such data from natural language and as a result, there has to be more research done in this aspect of things.
Reduces the number of redundant test cases from SDLC
One of the biggest potential applications of using AI in testing is that it can reduce the number of redundant test cases in the whole Software Development Life Cycle by identifying such duplicate cases automatically. As of now, it is slightly difficult to automate this process but with the right assessment of relevance of test data can help you solve the issue considerably.
It might take a couple of years to truly integrate this feature in the test cycles but it is surely something that can benefit everyone. However, with the automation of test cases, it has already freed up the human resources from indulging in repetitive tasks and has allowed them to instead use that time for creative and innovative works. Once the process of identifying the redundant test cases too is done by AI, it will further improve the execution of test cases.
Helps you reach the maximum potential of risk coverage
The current world of software testing needs faster results and for that you need faster test execution cycles. This is where AI comes into play by detecting defects in the test cases and identifying repetitive test cases. Furthermore, AI can keep track on risk contribution of different test cases and detect the average execution time of a possible test cases by analysing similar test cases from the past. This way, you can maximise risk coverage and improve the quality of the output.
With so many applications and benefits of using AI in testing, it is surely the future of the software testing. Yes, the investments can be high but estimates suggest that the ROI on these investments will be effectively good in the long run.
A software testing company focusing on integrating AI in their testing phase should be the one you should get in touch with while building a software. TestUnity is one such software testing company that does the job brilliantly so as to ensure faster and more reliable results than usual.