Real bug fixes found in open source repositories seem to be the perfect source for learning to localize and repair real bugs. Yet, the scale of existing bug fix collections is typically too small for training data-intensive neural approaches. Neural bug detectors are hence almost exclusively trained on artificial bugs, produced by mutating existing source code and thus easily obtainable at large scales. However, neural bug detectors trained on artificial bugs usually underperform when faced with real bugs. To address this shortcoming, we set out to explore the impact of training on real bug fixes at scale. Our systematic study compares neural bug detectors trained on real bug fixes, artificial bugs and mixtures of real and artificial bugs at various dataset scales and with varying training techniques. Based on our insights gained from training on a novel dataset of 33k real bug fixes, we were able to identify a training setting capable of significantly improving the performance of existing neural bug detectors by up to 170% on simple bugs in Python. In addition, our evaluation shows that further gains can be expected by increasing the size of the real bug fix dataset or the code dataset used for generating artificial bugs. To facilitate future research on neural bug detection, we release our real bug fix dataset, trained models and code.

@inproceedings{RichterW23,
  title = {How to Train Your Neural Bug Detector: Artificial vs Real Bugs},
  author = {Cedric Richter and Heike Wehrheim},
  booktitle = {ASE},
  year = {2023},
  doi = {10.1109/ase56229.2023.00104}
}