Data Mining Techniques in Software Defect Prediction

Abstract

Quality and reliability are the major challenges faced in a secure software development process. There are major software cost overruns when a software product with bugs in its various components is deployed at client's side. The software warehouse is commonly used as record keeping repository which is mostly required while adding new features or fixing bugs. Software bugs lead to inaccurate and different results. As an outcome, the software projects run late, are cancelled or becomes unreliable after deployment This paper entails a detailed literature review on the existence of software failures in the recent past, while briefly discussing about the severe repercussions of these failures. In order to identify these bugs timely, different data mining techniques have been discussed, and used by the researchers profusely, simultaneously with the bug tracking systems to locate the occurrence of the bugs precisely. Bug tracking system plays a vital role in software project as poorly designed bug tracking system are partly to be blame for the delay to resolve problem. Many researchers have suggested different ways to improve the bug tracking system. Bug repositories are major source of database which keeps the history of success and failure. This paper also discusses the interesting approaches to convert software repositories to active repositories and also discuss how systematic mining uncovers which modules are most prone to defects and failures. Diverse social and technical issues are associated with software failure and software defects are the major causes for the degradation of quality of product. In software engineering, most active research is software defect prediction. Bug fix time prediction model like pre-release, post-release defect and different metrices to predict failures is been discuss in this paper.