Entropy based bug prediction using support vector regression

Predicting software defects is one of the key areas of research in software engineering. Researchers have devised and implemented a plethora of defect/bug prediction approaches namely code churn, past bugs, refactoring, number of authors, file size and age, etc by measuring the performance in terms of accuracy and complexity. Different mathematical models have also been developed in the literature to monitor the bug occurrence and fixing process. These existing mathematical models named software reliability growth models are either calendar time or testing effort dependent. The occurrence of bugs in the software is mainly due to the continuous changes in the software code. The continuous changes in the software code make the code complex. The complexity of the code changes have already been quantified in terms of entropy as follows in Hassan [9]. In the available literature, few authors have proposed entropy based bug prediction using conventional simple linear regression (SLR) method. In this paper, we have proposed an entropy based bug prediction approach using support vector regression (SVR). We have compared the results of proposed models with the existing one in the literature and have found that the proposed models are good bug predictor as they have shown the significant improvement in their performance.