Survey on Software Defect Prediction Using Machine Learning Techniques

Software defect prediction plays an important role in improving software quality and it help to reducing time and cost for software testing. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. The ability of a machine to improve its performance based on previous results. Machine learning improves efficiency of human learning, discover new things or structure that is unknown to humans and find important information in a document. For that purpose, different machine learning techniques are used to remove the unnecessary, erroneous data from the dataset. Software defect prediction is seen as a highly important ability when planning a software project and much greater effort is needed to solve this complex problem using a software metrics and defect dataset. Metrics are the relationship between the numerical value and it applied on the software therefore it is used for predicting defect. The primary goal of this survey paper is to understand the existing techniques for predicting software defect.