From Commit Message Generation to History-Aware Commit Message
  Completion
Aleksandra Eliseeva Yaroslav Sokolov Egor Bogomolov Yaroslav Golubev Danny Dig Timofey Bryksin
Commit messages are crucial to software development, allowing developers to
track changes and collaborate effectively. Despite their utility, most commit
messages lack important information since writing high-quality commit messages
is tedious and time-consuming. The active research on commit message generation
(CMG) has not yet led to wide adoption in practice. We argue that if we could
shift the focus from commit message generation to commit message completion and
use previous commit history as additional context, we could significantly
improve the quality and the personal nature of the resulting commit messages.
  In this paper, we propose and evaluate both of these novel ideas. Since the
existing datasets lack historical data, we collect and share a novel dataset
called CommitChronicle, containing 10.7M commits across 20 programming
languages. We use this dataset to evaluate the completion setting and the
usefulness of the historical context for state-of-the-art CMG models and
GPT-3.5-turbo. Our results show that in some contexts, commit message
completion shows better results than generation, and that while in general
GPT-3.5-turbo performs worse, it shows potential for long and detailed
messages. As for the history, the results show that historical information
improves the performance of CMG models in the generation task, and the
performance of GPT-3.5-turbo in both generation and completion.