JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction
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A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-introducing. Recently, CC2Vec – a deep learning approach for Just-In-Time defect prediction – has been proposed. However, CC2Vec requires the whole dataset (i.e., training + testing) for model training, assuming that all unlabelled testing datasets would be available beforehand, which does not follow the key principles of just-in-time defect predictions. Our replication study shows that, after excluding the testing dataset for model training, the F-measure of CC2Vec is decreased by 38.5 highlighting the negative impact of excluding the testing dataset for Just-In-Time defect prediction. In addition, CC2Vec cannot perform fine-grained predictions at the line level (i.e., which lines are most risky for a given commit). In this paper, we propose JITLine – a Just-In-Time defect prediction approach for predicting defect-introducing commits and identifying lines that are associated with that defect-introducing commit (i.e., defective lines). Through a case study of 37,524 commits from OpenStack and Qt, we find that our JITLine approach is at least 26 cost-effective (PCI@20 approaches (i.e., CC2Vec and DeepJIT) and the fine-grained predictions at the line level by our approach are 133 the baseline NLP approach. Therefore, our JITLine approach may help practitioners to better prioritize defect-introducing commits and better identify defective lines.
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