A STUDY ON SOFTWARE DEFECT PREDICTION SYSTEM USING DATA MINING TECHNIQUES

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J. Mary Catherine, et. al.

Abstract

Defects in software modules are a source of significant concern. Software reliability and software quality assurance ensure the high quality of applications. A software defect triggers software malfunction in an executable product. A number of methods for forecasting machine faults have been suggested, but none have proven to be sufficiently accurate. In the design of software error prediction models, the aim is to use metrics that can be obtained comparatively early in the life cycle of software production to provide fair initial quality estimates of an evolving software framework.Here are various data mining classification and forecasting techniques. Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) have been analyzed and compared for software defect prediction model development. For this paper, the DATATRIEVETM project developed by Digital Engineering, Italy was used to validate the algorithm. The findings revealed that the model was an exceptional statistical model using the NN classification methodology. The main challenges faced in the secure software development process are quality and reliability. There are major software cost violations when a software product with errors in its various components is used on the customer’s side. The software warehouse is commonly used as a record keeping repository, which is often needed when adding new features or fixing bugs. Software errors can lead to erroneous and different results. As a result, software programs run late, are canceled, or become unreliable after use. Different social and technical issues are associated with software failure and software defects are the main reasons for deteriorating product quality. In software engineering, the most active research in software domain is defect prediction.This study discusses the bug-fix time forecast model, pre-release release, post-release error and different measurements to predict failures. Predicted results help developers identify and fix potential vulnerabilities, thereby improving software stability and reliability.

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