Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time
  Defect Prediction
Xin Zhou DongGyun Han David Lo
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.