An Extended Survey Concerning the Significance of Artificial Intelligence and Machine Learning Techniques for Bug Triage and Management

Abstract

Bug reports are generated in large numbers during the software development processes in the software industry. The manual processing of these issues is usually time consuming and prone to errors, consequently delaying the entire software development process. Thus, a properly designed bug triage and management process implies that essential operations, such as duplicate detection, bug assignments to proper developers, and determination of the importance level, are sustained by efficient algorithmic models and implementation approaches. Designing and implementing a proper bug triage and management process becomes an essential scientific research topic, as it may significantly optimize the software development and business process in the information technology industry. Consequently, this paper thoroughly surveys the most significant related scientific contributions analytically and constructively, distinguishing it from similar survey papers. The paper proposes optimal algorithmic and software solutions for particular real-world use cases that are analyzed. It concludes by presenting the most important open research questions and challenges. Additionally, the paper provides a valuable scientific literature survey for any researcher or practitioner in software bug triage and management systems based on artificial intelligence and machine learning techniques.

Keywords

• Bug report
• bug prioritization
• bug assignment
• bug triaging
• classification
• machine learning