Building sustainable software testing using machine learning for green engineering

Software testing is a very time and resource consuming activity in software development process. More testing tools will be applied to perform different kinds of testing procedures with respect to selected test cases. The more execution of testing tools may produce more heat by consuming more electricity for executing complete application to test functional and nonfunctional requirements proposed by customers. To save energy and environment, a sustainable process would be built in software development process. For that, the machine learning (ML) models are suggested to select optimized test suites and testing tools. It can be achieved by collecting enough information from the conducted software testing procedures on different applications. This historical data have to be identified and continuously grown in size for giving as input to the deployed ML model for helping in ‘software testing process’. For implementing this in ‘software testing process’, the experts in the ML area will be appointed to take sufficient actions in processing the collected data for building ML models.