Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models
Proceedings of the 2016 24th ACM SIGSOFT international symposium on …, 2016•dl.acm.org
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 …
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 …
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.
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