Representing code changes as numeric feature vectors, i.e., code change
representations, is usually an essential step to automate many software
engineering tasks related to code changes, e.g., commit message generation and
just-in-time defect prediction. Intuitively, the quality of code change
representations is crucial for the effectiveness of automated approaches. Prior
work on code changes usually designs and evaluates code change representation
approaches for a specific task, and little work has investigated code change
encoders that can be used and jointly trained on various tasks. To fill this
gap, this work proposes a novel Code Change Representation learning approach
named CCRep, which can learn to encode code changes as feature vectors for
diverse downstream tasks. Specifically, CCRep regards a code change as the
combination of its before-change and after-change code, leverages a pre-trained
code model to obtain high-quality contextual embeddings of code, and uses a
novel mechanism named query back to extract and encode the changed code
fragments and make them explicitly interact with the whole code change. To
evaluate CCRep and demonstrate its applicability to diverse code-change-related
tasks, we apply it to three tasks: commit message generation, patch correctness
assessment, and just-in-time defect prediction. Experimental results show that
CCRep outperforms the state-of-the-art techniques on each task.

此研究使用 CCRep 方法，通过将代码更改表示为特征向量，学习在多种软件工程任务上使用的高质量的代码更改表示方法。实验结果表明 CCRep 在各任务上表现优异。