Software Bug Categorization Technique Based on Fuzzy Similarity

Categorization of software bugs is an important task in software repository mining. Most of the information about the software bugs are in textual form, and it is difficult to categorize these bugs into a particular category as the some of the terms present in the software bugs can be common to multiple categories. Fuzzy similarity technique can be utilized to identify the belongingness of these bugs into different categories. In this paper, a binary software bug categorization technique using fuzzy similarity measure is proposed to classify the bugs as bugs or non-bugs. The fuzzy similarity of a software bug is computed and based on a user-defined threshold value the bug can either be assigned to bug or non-bug category. Experiments are performed on available software bug data sets and performance of proposed fuzzy similarity based classifier is evaluated using the parameters accuracy, F-measure, precision, and recall. The proposed algorithm is also compared with the existing standard machine learning algorithms.