Track: Computers and Computing
Abstract
A defective module not only increases the development time and development cost but also increases the maintenance time and maintenance cost. According to the available literature survey, many systems failed due to schedule and time budget overruns. Therefore a software defect detection technique is needed to identify those software modules that are very likely to include defects and thereby improves the software quality by contributing in the efficient removal software defects. The main objective of the paper is to help software developers in identifying the software defects based on the various software metrics using various classification and machine learning techniques. In this paper, we are performing empirical classification comparison on 5 real world datasets.