Automating Bug Triage: Unleashing the Power of Machine Learning and Natural Language Processing

Abstract

This paper explores the potential of automating bug triage using machine learning and natural language processing. In the context of growing software complexity, manual bug handling faces challenges leading to delays. The study critically examines traditional bug triage methods, emphasizing their limitations and the need for automated solutions. By reviewing various machine learning techniques applied in bug triage, the paper provides insights for practitioners. The proposed Bug Triage model, integrating Word2Vec embeddings and XGBoost, shows promising results in categorizing and prioritizing bug reports. The paper concludes by highlighting future research opportunities to advance bug triage methodologies and improve software development practices.