Improving software automation testing using Jenkins, and machine learning under big data

Abstract

Software testing is an essential phase of software development life cycle that ensures quality of the software by fixing bugs which can be done with automated testing to reduce human intervention and to save time and effort consumed in the manual testing. The entire process of testing can be automated easily with the help of automated testing tools. This paper provides a feasibility study for the most commonly used testing tools, we conducted a comparative study of five open source tools to determine their usability and effectiveness. Another point discussed in our paper is the use of machine learning under big data in order to make the system intelligent so that tests lend themselves to automation. We will show how can the combination of all these mentioned technologies can help users to decide which strategy to go for to save both cost and time during testing phases.