Effort-aware just-in-time defect prediction : simple unsupervised models could be better than supervised models

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.