Topic-based Defect Prediction

Tung T. Nguyen, Tien N. Nguyen, and Tu M. Phuong
Iowa State University, USA; Posts and Telecommunications Institute of Technology, Vietnam
Empirical SE

Defects are unavoidable in software development and fixing them is costly and resource-intensive. To build defect pre- diction models, researchers have investigated a number of factors related to the defect-proneness of source code, such as code complexity, change complexity, or socio-technical factors. In this paper, we propose a new approach that emphasizes on technical concerns/functionality of a system. In our approach, a software system is viewed as a collection of software artifacts that describe different technical concerns/aspects. Those concerns are assumed to have different levels of defect-proneness, thus, cause different levels of defect-proneness to the relevant software artifacts. We use topic modeling to measure the concerns in source code, and use them as the input for machine learning-based defect prediction models. Preliminary result on Eclipse JDT shows that the topic-based metrics have high correlation to the number of bugs (defect-proneness), and our topic-based defect prediction has better predictive performance than existing state-of-the-art approaches.