Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction
This work addresses defect prediction in software engineering by integrating complementary approaches, though it is incremental as it builds on existing methods.
The paper tackles the problem of Just-In-Time defect prediction by proposing SimCom++, a model fusion framework that combines expert knowledge-based simple models with deep learning-based complex models, achieving performance improvements of 5.7–26.9% over baselines.
Just-In-Time (JIT) defect prediction aims to automatically predict whether a commit is defective or not, and has been widely studied in recent years. In general, most studies can be classified into two categories: 1) simple models using traditional machine learning classifiers with hand-crafted features, and 2) complex models using deep learning techniques to automatically extract features from commit contents. Hand-crafted features used by simple models are based on expert knowledge but may not fully represent the semantic meaning of the commits. On the other hand, deep learning-based features used by complex models represent the semantic meaning of commits but may not reflect useful expert knowledge. Simple models and complex models seem complementary to each other to some extent. To utilize the advantages of both simple and complex models, we propose a model fusion framework that adopts both early fusions on the feature level and late fusions on the decision level. We propose SimCom++ by adopting the best early and late fusion strategies. The experimental results show that SimCom++ can significantly outperform the baselines by 5.7--26.9\%. In addition, our experimental results confirm that the simple model and complex model are complementary to each other.