How to select the best AI tool to meet the QA requirements?
AI, Software Quality Assurance In the traditional setup of executing software quality assurance, a lot of data and information are often not considered for further analysis. This shortcoming bears a negative impact on the QA efficiency such that the stability of application can be called to question. The data and information appearing in the form of test results, defect logs, project documentation, and production incidents are voluminous and complex to decipher and analyze. Even test automation as a part of the Agile or DevOps set up cannot be of help here, for it only executes pre-written test scripts iteratively to generate outcomes that may or may not meet the test objectives. Where typical software test assurance can fail, artificial intelligence and machine learning can chip in to ensure enhanced QA efficiencies and better identification of glitches, defect hotspots, and failure patterns. In fact, the AI-based algorithms can analyze the wealth of informat...