CNN based fault-prone commit detection

Defect prediction is an important task for preserving software quality.
A lot of previous research has analyzed source code to predict defects; however,
it contains a problem that is its prediction grain is too coarse and its feedback is too late
for software developers.
To achieve a more fine-grained prediction and an earlier feedback, several approaches that analyzes source code changes has been reported.
Those approaches have applied various machine learning techniques and
deep learning techniques to change metrics, such as the number of lines added,
modified files, and modified directories.
In this paper, we propose a novel approach for defect prediction called Word-Convolutional Neural Network (W-CNN), which applies CNN to the modified source code itself.
Our evaluation results show that the proposed approach can improve the effectiveness of defect prediction with a small overhead on the prediction time.

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