Deep learning-based software bug classification

Context: Accurate classification of bugs can help accelerate the bug triage process, code inspection, and repair activities. In this context, many machine learning techniques have been proposed to classify bugs. The expressive power of deep learning could be used to further improve classification.Objective: We propose a novel deep learning-based bug classification approach.Methods: We first build a bug taxonomy with eight bug classes, each characterized by a set of keywords. Subsequently, we heuristically annotate a moderately large set (similar to 1.36M) of software bug resolution reports using an earth-mover distance technique based on the keywords. Finally, we use four attention-based classification techniques to classify these curated bugs.Results: Our experiments on a carefully collected dataset indicate that our proposed technique achieved a mean F1-Score of 84.78% and a mean macro-average ROC of 98.25%.Conclusion: Our proposed approach was observed to outperform the existing techniques by 16.88% on an average in terms of F1-Score for the considered dataset.

Automated summary

Accurate bug classification is important for speeding up bug triage, code inspection, and repair tasks. To improve classification, this study proposes a novel bug classification approach based on deep learning. The approach includes building a bug taxonomy with eight bug classes using keywords, annotating a large set of bug resolution reports, and utilizing attention-based classification techniques. Experimental results show that the proposed technique outperforms existing methods in terms of F1-Score by an average of 16.88% on the considered dataset.