Human-in-the-loop online just-in-time software defect prediction

TL;DR

This paper introduces a human-in-the-loop approach for online just-in-time software defect prediction, incorporating SQA staff feedback and a robust evaluation framework to improve prediction accuracy and reliability.

Contribution

It proposes a novel HITL method for O-JIT-SDP and a comprehensive evaluation framework using bootstrap and statistical tests for better model assessment.

Findings

HITL feedback improves prediction accuracy.

The evaluation framework enhances model credibility.

Experimental results across 10 projects validate the approach.

Abstract

Online Just-In-Time Software Defect Prediction (O-JIT-SDP) uses an online model to predict whether a new software change will introduce a bug or not. However, existing studies neglect the interaction of Software Quality Assurance (SQA) staff with the model, which may miss the opportunity to improve the prediction accuracy through the feedback from SQA staff. To tackle this problem, we propose Human-In-The-Loop (HITL) O-JIT-SDP that integrates feedback from SQA staff to enhance the prediction process. Furthermore, we introduce a performance evaluation framework that utilizes a k-fold distributed bootstrap method along with the Wilcoxon signed-rank test. This framework facilitates thorough pairwise comparisons of alternative classification algorithms using a prequential evaluation approach. Our proposal enables continuous statistical testing throughout the prequential process, empowering developers to make real-time decisions based on robust statistical evidence. Through experimentation across 10 GitHub projects, we demonstrate that our evaluation framework enhances the credibility of model evaluation, and the incorporation of HITL feedback elevates the prediction performance of online JIT-SDP models. These advancements hold the potential to significantly enhance the value of O-JIT-SDP for industrial applications.