Defective software modules can leads to ad hoc software failures, shoots up development & maintenance cost and result in customer dissatisfaction. Defect mapping and awareness of its impact in different business applications paves way to improve its quality. Previous researches show that it has treated all bugs alike. Proper Identification and categorization helps to handle and fix bugs diligently. Evaluation of prediction techniques is mainly based on precision and recall measures. It focuses on the defects in a software system. A prediction of the number of left-out defects in an inspected arte fact can be judiciously used for decision making. An accurate prediction of quantum of defects during testing a software product contributes not only to manage the system testing process but also to estimate its required maintenance. It goes a long way to improve software quality and testing efficiency by building predictive models from code attributes to timely identification of fault-prone modules. In short, this paper provides the prediction of bugs by using data mining techniques such as Association Mining, Classification and Clustering. This complements developers to detect software defects and debug them. Unsupervised techniques come handy for defect prediction in software modules, on a large scale in those cases where defect labels are not present.

Software Defect Prediction, Bugs, Software Repositories, Data Mining, Classification, Clustering, Association Mining

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