Applying Large Language Models API to Issue Classification Problem
Gabriel Aracena Kyle Luster Fabio Santos Igor Steinmacher Marco A. Gerosa
Effective prioritization of issue reports is crucial in software engineering
to optimize resource allocation and address critical problems promptly.
However, the manual classification of issue reports for prioritization is
laborious and lacks scalability. Alternatively, many open source software (OSS)
projects employ automated processes for this task, albeit relying on
substantial datasets for adequate training. This research seeks to devise an
automated approach that ensures reliability in issue prioritization, even when
trained on smaller datasets. Our proposed methodology harnesses the power of
Generative Pre-trained Transformers (GPT), recognizing their potential to
efficiently handle this task. By leveraging the capabilities of such models, we
aim to develop a robust system for prioritizing issue reports accurately,
mitigating the necessity for extensive training data while maintaining
reliability. In our research, we have developed a reliable GPT-based approach
to accurately label and prioritize issue reports with a reduced training
dataset. By reducing reliance on massive data requirements and focusing on
few-shot fine-tuning, our methodology offers a more accessible and efficient
solution for issue prioritization in software engineering. Our model predicted
issue types in individual projects up to 93.2% in precision, 95% in recall, and
89.3% in F1-score.