The role of Artificial Intelligence in Quality Assurance

1. The role of Artificial Intelligence in Quality Assurance

2. Introduction Software testing efficiency and software testing effectiveness are two key metrics that determine the overall progress of a test strategy. Artificial Intelligence (AI) and Machine Learning (ML) in testing essentially focus on these two parameters. AI & ML can optimize risk coverage, prevent redundancies, perform portfolio inspection, detect false positives, diagnose defects automatically, and analyse user experience.  It is estimated that more than 60% of the test cases in an enterprise test case portfolio are redundant. AI identifies such test cases that are physically as well as logically identical and eliminates the duplicates, which do not add any business value and can be removed without decreasing the business risk coverage. AI is capable of maximizing defect detection and risk coverage while minimizing costs, execution time, and the number of test cases by identifying the optimal test sets. It can uncover weak spots in test case portfolios by tracking flaky test cases, unused test cases, untested requirements, and those test cases that are not linked to the requirements. Additionally, AI has self-healing automation properties, which means it can heal the broken automated test cases and make test automation better resilient to changes. 

3. The present state of AI-driven software testing AI has been buzzing around since the 1900s and it still upholds the hype across the globe. Everyone keeps talking about the possibilities of the role of AI. However, there is still a wide gap between where AI has reached today and where it has to go. Kevin explains the present state of AI in software testing as, “The vision, the hope for everybody is that someday, AI will be able to do the testing for us. We’re not there yet. I’m not promoting that. But what is definitely here is AI-based tools and AI that helps us with our jobs. So, we shouldn’t look at it as AI replacing testers yet, we shouldn’t look at it as AI replacing really most of our processes yet. What AI does right now is it helps us be better testers, meaning it takes out some of that mundane work that we wouldn’t like to do anyway. Or maybe as we’ll hear a little bit later, AI can help us do things like prediction or analytics better than we’ve done in the past, which just allows us to do our jobs better.” 

4. AI resolving the ‘Test Automation trap’ Software testing is a time-consuming and cost-intensive activity. A challenge with regular test automation is that by the time the test code is completed, the requirements start changing and applications start evolving with regards to business functionality and UI. This means that the whole effort put into developing the test code goes into vain and you need to adapt the test automation needs accordingly. Kalyan calls it the ‘Test Automation trap”. He explains, “Test automation trap is when the test teams are not getting enough time to be able to do the failure triage from the previous test run before building the next test automation code. That is where AI can be really used to solve this dilemma and to accelerate the manual testing. With some of our clients, we are able to apply AI in the context of prioritizing test cases and also maintaining the test automation code in an automated manner, as opposed to manually investigating what needs to be changed. And I expect that over a period of time, we’ll see that it can play a great role in the context of analyzing the test results and also deciding on what needs to be tested and things like that, which can happen freely without human intervention.”  Read Full Blog at: https://www.cigniti.com/blog/artificial-intelligence-qa-testing/