Published on December 2020 | Artificial Intelligence, Machine Learning

Pharmacovigilance Based Artificial Intelligence And Machine Learning In Software Testing - An Empirical View

The test cycle in software engineering process would progress if testing is automated and thus consequences in improvement in automatic data access, test run and testing cycle. The process of test creation and increase in case coverage can be done through new contexts and procedures to improve quality assurance. Introducing the self-learning algorithms will move the testing mechanism to next new level by incorporating many automated tools and thus usage of test automation is in high demand. This paper reveals and explores the areas and tools in software testing where pharmacovigilance based artificial intelligence/machine learning integration can be used for quality assurance.