Just-in-Time and Real-Time Bug-Inducing Commit Prediction Using a Federated Learning Approach
Md Rayhan Islam, Banani Roy, Mohammad Muntasir Hassan, Amin Nikanjam · 2024
Previous studies proposed different methods for predicting Just in Time (JIT) and Real-Time (RT) bug-inducing commits to reduce maintenance costs. While Machine Learning (ML) models have been used to predict bug-inducing commits, a large and robust dataset is required to train the models. Unfortunately, it is difficult for individuals to collect such large datasets due to privacy concerns when sharing data, especially from software projects across multiple organizations. In this regard, during model training, Federated Learning (FL) has been introduced to train collectively to overcome data-sharing limitations due to privacy concerns. In this paper, we apply FL to predict JIT and RT bug-inducing commits and to understand if FL can be a viable solution to overcome the privacy limitations in this domain. In this study, we compare a few standalone ML models, such as Logistic Regression (LR) and Deep Learning (DL), with their respective FL models, namely Federated Logistic Regression (FL-LR) and Federated Deep Learning (FL-DL) for JIT and RT bug-inducing commit prediction. We also compare different aggregation strategies like FedAvg and FedAvgM for FL. The study uses 126,103 commits from 22 projects across three benchmark datasets from various domains. Our study suggests that the federated approach enhances model performance for cross-project commits and reduces training time. The FL models maintain consistency across diverse application domains. Furthermore, aggregation strategies like FedAvgM are beneficial to improve model accuracy for JIT and RT bug-inducing commit prediction.