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Title: Effort-aware just-in-time defect prediction : simple unsupervised models could be better than supervised models
Authors: Yang, Y
Zhou, Y
Liu, J
Zhao, Y
Lu, H
Xu, L
Xu, B
Leung, H
Keywords: Changes
Issue Date: 2016
Publisher: Association for Computing Machinery
Source: Proceedings of the ACM SIGSOFT Symposium on the Foundations of Software Engineering, 2016, v. 13-18-November-2016, p. 157-168 How to cite?
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 su-pervised 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 set-tings 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.
Description: 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016, Seattle, US, 13-18 November 2016
ISBN: 9781450342186
DOI: 10.1145/2950290.295035
Appears in Collections:Conference Paper

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