An Ensemble Method for Bug Triaging using Large Language Models

Abstract

This study delves into the automation of bug triaging - the process of assigning bug reports to appropriate developers and components in software development. At the core of our investigation are six transformer-based Large Language Models (LLMs), which we fine-tuned using a sequence classification method tailored for bug triaging tasks. Our results demonstrate a noteworthy performance of the DeBERTa model, which significantly outperforms its counterparts CodeBERT, DistilBERT, RoBERTa, ALBERT, and BERT in terms of effectiveness. However, it is crucial to note that despite the varying performance of each model, each model exhibits a unique degree of orthogonality, indicating distinct strengths in their bug triaging capabilities. Leveraging these orthogonal characteristics, we propose an ensemble method combining these LLMs through voting and stacking techniques. Remarkably, our findings reveal that the voting-based ensemble method surpasses all individual baselines in performance.