Predicting software defect type using concept-based classification

Automatically predicting the defect type of a software defect from its description can significantly speed up and improve the software defect management process. A major challenge for the supervised learning based current approaches for this task is the need for labeled training data. Creating such data is an expensive and effort-intensive task requiring domain-specific expertise. In this paper, we propose to circumvent this problem by carrying out concept-based classification (CBC) of software defect reports with help of the Explicit Semantic Analysis (ESA) framework. We first create the concept-based representations of a software defect report and the defect types in the software defect classification scheme by projecting their textual descriptions into a concept-space spanned by the Wikipedia articles. Then, we compute the “semantic” similarity between these concept-based representations and assign the software defect type that has the highest similarity with the defect report. The proposed approach achieves accuracy comparable to the state-of-the-art semi-supervised and active learning approach for this task without requiring labeled training data. Additional advantages of the CBC approach are: (i) unlike the state-of-the-art, it does not need the source code used to fix a software defect, and (ii) it does not suffer from the class-imbalance problem faced by the supervised learning paradigm.