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