On Refining the SZZ Algorithm with Bug Discussion Data.

Pooja RaniFernando PetrulioAlberto Bacchelli

Published in: Empirical software engineering (2024)

We define a taxonomy outlining the rationale behind developers' references to diverse files in their discussions. We observe that bug discussions often mention files relevant to enhancing the SZZ algorithm's efficacy. Then, we verify that integrating these file references augments the precision of SZZ in pinpointing bug-introducing commits. Yet, it does not markedly influence recall. These results deepen our comprehension of the usefulness of bug discussions for SZZ. Future work can leverage our dataset and explore other techniques to further address the problem of tangled commits and ghost commits. Data & material: https://zenodo.org/records/11484723.

Keyphrases

machine learning
electronic health record
advance care planning
deep learning
big data
clinical trial
neural network
artificial intelligence