QA 2.0: Smarter Testing with AI — Test Case Generation, Defect Prediction, and Automation
The use of Artificial Intelligence (AI) in Quality Assurance (QA) has gained significant attention in recent years. AI has emerged as a powerful tool that can improve the efficiency and effectiveness of QA processes. From test case generation to defect prediction and test automation, AI is revolutionising how we approach QA. However, as with any transformative technology, there are concerns about job displacement and the over-reliance on AI tools. In this article, I write about the benefits and challenges of using AI in QA. I also attempt to explore how organizations can strike a balance between leveraging AI and preserving human expertise.
Test Case Generation: Unleashing the Power of AI
Traditionally, test case generation has been a manual and time-consuming process. Testers would analyze requirements, design test scenarios, and generate test cases manually. AI-powered tools can now automate these processes, analyzing vast amounts of data to generate test cases more efficiently and accurately.
By leveraging machine learning algorithms, AI tools can analyze existing test cases, requirements, and even code to generate new test cases. These AI algorithms can identify patterns, predict potential defects, and…
