Kavli Affiliate: Zhuo Li
| First 5 Authors: Jia Li, Ge Li, Zhuo Li, Zhi Jin, Xing Hu
| Summary:
Developers often perform repetitive code editing activities for various
reasons (e.g., code refactoring) during software development. Pre-trained code
editing models have achieved the state-of-the-art (SOTA) results. Pre-trained
models are first pre-trained with pre-training tasks and fine-tuned with the
code editing task. Existing pre-training tasks mainly are code infilling tasks
(e.g., masked language modeling), which are derived from the natural language
processing field and are not designed for automatic code editing.
This paper proposes a novel pre-training task specialized in code editing and
presents an effective pre-trained code editing model named CodeEditor. Our
pre-training task further improves the performance and generalization ability
of code editing models. Specifically, we collect lots of real-world code
snippets as the ground truth and use a powerful generator to rewrite them into
mutated versions. Then, we pre-train our CodeEditor to edit mutated versions
into the corresponding ground truth, to learn edit patterns. We conduct
experiments on four code editing datasets and evaluate the pre-trained
CodeEditor in three settings. (1) In the fine-tuning setting, we train the
pre-trained CodeEditor with four datasets and evaluate it on the test data.
CodeEditor outperforms the SOTA baselines by 15%, 25.5%, and 9.4% and 26.6% on
four datasets. (2) In the few-shot setting, we train the pre-trained CodeEditor
with limited data and evaluate it on the test data. CodeEditor substantially
performs better than all baselines. (3) In the zero-shot setting, CodeEditor
correctly edits 1,113 programs while the SOTA baselines can not work.
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