  Code revert prediction, a specialized form of software defect detection, aims
to forecast or predict the likelihood of code changes being reverted or rolled
back in software development. This task is very important in practice because
by identifying code changes that are more prone to being reverted, developers
and project managers can proactively take measures to prevent issues, improve
code quality, and optimize development processes. However, compared to code
defect detection, code revert prediction has been rarely studied in previous
research. Additionally, many previous methods for code defect detection relied
on independent features but ignored relationships between code scripts.
Moreover, new challenges are introduced due to constraints in an industry
setting such as company regulation, limited features and large-scale codebase.
To overcome these limitations, this paper presents a systematic empirical study
for code revert prediction that integrates the code import graph with code
features. Different strategies to address anomalies and data imbalance have
been implemented including graph neural networks with imbalance classification
and anomaly detection. We conduct the experiments on real-world code commit
data within J.P. Morgan Chase which is extremely imbalanced in order to make a
comprehensive comparison of these different approaches for the code revert
prediction problem.
