The implementation of clustering algorithm in mutation testing.

Abstract

Software testing phase is one of the most significant phases within the System Development Life Cycle (SDLC) since software bugs can be costly and traumatic. Originally, the implementation of software testing was done manually where the test cases are executed manually without any support from scripts or tools and then multiples of tools were introduced to help the implementation where automated testing taking place throughout the process where test cases are executed using the scripts, tools and software to provide the level of product's quality. Now with the widely known Artificial Intelligence (AI) technologies gaining place in industry, it is transforming the software testing in ways that could not have been dreamt of a decade ago including reducing the need for test maintenance, simplifying test creation, and driving new ways to assess the results. Machine learning is one of the branches in AI that happen to be a new dominated technology in industry. The use of machine learning in software testing's process is rarely implemented in the market, machine learning focus on the use of data and algorithms to imitate the way that humans learn, gradually improve its accuracy and it is an important component of the growing field of data science. In machine learning there are multiple algorithms to be use as an approach that can help to improve the way to manage and understand the results produced from the testing. The use of machine learning in this area theoretically able to provide a faster and easier test creation, make the test analysis much simpler and reducing the needs of test maintenance. In this study will be focusing on using clustering which is one of algorithm in machine learning as an approach to increase the mutation score of mutation testing which is one of the software testing methods on fault-based testing.