Software defect prediction using data mining techniques

Abstract

The success of any software system entirely depends on the accuracy of the results of the system and whether it is without any flaws. Software defect prediction problems have an extremely beneficial research potential. Software defects are the major issue in any software industry. Software defects not only reduce the software quality, increase costing but it also suspends the development schedule. Software bugs lead to inaccurate and discrepant results. As an outcome of this, the software projects run late, are cancelled or become unreliable after deployment. Quality and reliability are the major challenges faced in a secure software development process. There are major software cost overruns when a software product with bugs in its various components is deployed at client s side. The software warehouse is commonly used as record keeping repository which is mostly required while adding new features or fixing bugs. Many data mining techniques and dataset repository are available to predict the software defects. Bug prediction technique is an important part in software engineering area for last one decade. Software bugs which detect at early stage are simple and inexpensive for rectifying the software. Software quality can be enhanced by using the bug prediction techniques and the software bug can be reduced if applied accurately. Dependent and independent variable are considered in Software bug prediction. To prevent defect based on software metrics software prediction model are used. Metrics based classification categorize component as defective and non-defective. newline