FSE 2016 All Events

24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2016), November 13–18, 2016, Seattle, WA, USA

Desktop Layout

Session 3: Bug Detection and Debugging
Research Papers
Emerald 3, Chair: Tingting Yu
Effort-Aware Just-in-Time Defect Prediction: Simple Unsupervised Models Could Be Better Than Supervised Models
Yibiao Yang, Yuming Zhou, Jinping Liu, Yangyang Zhao, Hongmin Lu, Lei Xu, Baowen Xu, and Hareton Leung
(Nanjing University, China; Hong Kong Polytechnic University, China)
Publisher's Version
Abstract: Unsupervised models do not require the defect data to build the prediction models and hence incur a low building cost and gain a wide application range. Consequently, it would be more desirable for practitioners to apply unsupervised models in effort-aware just-in-time (JIT) defect prediction if they can predict defect-inducing changes well. However, little is currently known on their prediction effectiveness in this context. We aim to investigate the predictive power of simple unsupervised models in effort-aware JIT defect prediction, especially compared with the state-of-the-art supervised models in the recent literature. We first use the most commonly used change metrics to build simple unsupervised models. Then, we compare these unsupervised models with the state-of-the-art supervised models under cross-validation, time-wise-cross-validation, and across-project prediction settings to determine whether they are of practical value. The experimental results, from open-source software systems, show that many simple unsupervised models perform better than the state-of-the-art supervised models in effort-aware JIT defect prediction.


Time stamp: 2019-05-19T02:55:10+02:00