Abstract-Software defect prediction work focuses on the number of defects remaining in a software system. The software defect prediction model helps in early detection of defects and contributes to their efficient removal and producing a quality software system based on several metrics. A prediction of the number of remaining defects in an inspected are fact can be used for decision making. An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product's required maintenance. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. It strives to improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of faultprone modules. The main objective of paper is to help developers identify defects based on existing software metrics using data mining techniques and thereby improve the software quality. In this paper, we will discuss data mining techniques that are association mining, classification and clustering for software defect prediction. This helps the developers to detect software defects and correct them.
The field of software engineering concern with designing, developing, maintaining and modifying software. There are numerous types of data available in software engineering such as graphs, text, facts and figures. Meaningful information can be exacted from this complex data using well established data mining techniques such as association, classification, clustering etc. By uncovering hidden patterns using data mining software engineering data is made actionable. There are various goals in software engineering such as optimization, documentation, cost estimation etc. Selection of best mining method in each phase of software development lifecycle helps in achieving these goals efficiently and the success rate of software is increased. Various software engineering tasks are improved using data mining techniques. In this paper, the focus is how data mining techniques helps in achieving the software engineering goals and benefit the software engineering tasks.
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