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 analyze user experience. It is estimated that more than 60% of the test cases in an enterprise test case portfolio are redundant. AI identifies…

They say quality is everyone’s responsibility. But what if everyone fulfills this responsibility in their own way, without following any pre-determined guidelines, industry best practices, or protocols? Due to such an individualized approach, the outcomes of the Quality Assurance process will not be aligned to the overall organization’s objectives and goals. As each team or unit would use their choice of tools, platforms, and reporting mechanisms, there will be a lack of a standard way…

They say quality is everyone’s responsibility. But what if everyone fulfills this responsibility in their own way, without following any pre-determined guidelines, industry best practices, or protocols? Due to such an individualized approach, the outcomes of the Quality Assurance process will not be aligned to the overall organization’s objectives and goals. As each team or unit would use their choice of tools, platforms, and reporting mechanisms, there will be a lack of a standard way…