Existing work on software patches often use features specific to a single
task. These works often rely on manually identified features, and human effort
is required to identify these features for each task. In this work, we propose
CC2Vec, a neural network model that learns a representation of code changes
guided by their accompanying log messages, which represent the semantic intent
of the code changes. CC2Vec models the hierarchical structure of a code change
with the help of the attention mechanism and uses multiple comparison functions
to identify the differences between the removed and added code.
To evaluate if CC2Vec can produce a distributed representation of code
changes that is general and useful for multiple tasks on software patches, we
use the vectors produced by CC2Vec for three tasks: log message generation, bug
fixing patch identification, and just-in-time defect prediction. In all tasks,
the models using CC2Vec outperform the state-of-the-art techniques.

使用人工识别特征并需要为每个任务付出人力劳动的软件补丁现有方法存在缺陷，本文提出一种基于神经网络的 CC2Vec 模型，通过伴随的日志消息学习代码更改的表示，以识别代码更改的层次结构并使用多个比较函数来识别删除和添加代码之间的差异，并证明该模型在多个软件补丁任务中均优于现有技术。