Fault localization in software testing using soft computing approaches

Abstract

Testing is the most important and critical task in software development life cycle. Whenever software testing execution fails its test scripts is analyzed so that the point where fault occurred can be detected and the expected result can be achieved. Detecting fault in software is called as fault localization. Manually fault localization can be a cumbersome job so providing automated technique to do the same without human intervention is the demand from long time. In this paper, a brief overview of some important fault localization technique using soft computing techniques is carried out. Based on the identified points, it is identified that better result may be generated using machine learning technique along with time reduction. Prime objective of this paper is to made and attempt for identifying the fault localization techniques in combination with soft computing approaches to minimize the time and space complexities, so that the better results may be achieved in context of usability and effectiveness.