Topic-based defect prediction (NIER track)

  • Authors:

  • Tung Thanh Nguyen;Tien N. Nguyen;Tu Minh Phuong

  • Affiliations:

  • Iowa State University, Des Moines, IA, USA;Iowa State University, Des Moines, IA, USA;Posts and Telecommunications Institute of Technology, Vietnam, Vietnam

  • Venue:
  • Proceedings of the 33rd International Conference on Software Engineering
  • Year:
  • 2011

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Abstract

Defects are unavoidable in software development and fixing them is costly and resource-intensive. To build defect prediction 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 defectproneness 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.