METHOD LEVEL BUG PREDICTION USING INFORMATION GAIN
Author’s Name : Vaijayanthi Murugan | Karthick M
Volume 04 Issue 01 Year 2017 ISSN No: 2349-3828 Page no: 9-13
Software defects commonly known as bugs, present a serious challenge for the software developers to predict the bugs and to enhance the system reliability and dependability. The software defects are usually an incorrect output value, exceptions occurred in the source code, failure due to logical errors or due to any syntax errors. As the size of the programs grows and it may contain large number of methods, so, occurrence of bugs become more common and difficult to fix. It will take time to predict the bugs at the individual methods. Many techniques have been developed to mainly focus on method-level bug prediction. Several features are commonly used for method level bug prediction. To identify the best set of features it is proposed to use Filter Based Feature Selection (FBFS) using Information Gain. The Information Gain value is calculated for estimating the individual features. Based on the Information Gain values, the relevant features will be extracted for evaluation. In this work, the method-level bug prediction will be carried out using Support Vector Machine (SVM) classifier. Finally, the performance of the bug prediction models will be measured by using Precision, Recall and F-measure values. The volume of predicted bugs can be assessed by using the values of evaluation measures.
Bug prediction, precision, recall, F-measure, method-level, information gain, accuracy, SVM classifier.
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