Predictive data mining model for software bug estimation using average weighted similarity

Software bug estimation is a very essential activity for effective and proper software project planning. All the software bug related data are kept in software bug repositories. Software bug (defect) repositories contains lot of useful information related to the development of a project. Data mining techniques can be applied on these repositories to discover useful interesting patterns. In this paper a prediction data mining technique is proposed to predict the software bug estimation from a software bug repository. A two step prediction model is proposed In the first step bug for which estimation is required, its summary and description is matched against the summary and description of bugs available in bug repositories. A weighted similarity model is suggested to match the summary and description for a pair of software bugs. In the second step the fix duration of all the similar bugs are calculated and stored and its average is calculated, which indicates the predicted estimation of a bug. The proposed model is implemented using open source technologies and is explained with the help of illustrative example.