Triage in Software Engineering: A Systematic Review of Research and Practice

2025-11-05

AI Summary

Software issue triage faces challenges of low operational efficiency and a persistent academic–industrial gap in complex system maintenance. To address this, we conduct a systematic literature review (SLR) spanning 234 English and Chinese publications from 2004 to 2023. This is the first SLR to jointly analyze academic research and industrial practice, revealing three critical bottlenecks: misaligned objectives between academia and industry, lack of standardized evaluation criteria, and practical deployment barriers. We propose a unified triage evaluation framework structured along four dimensions—data, tasks, metrics, and benchmarks—and systematically catalog open-source datasets and empirical methodologies to enable reproducible performance validation. All reviewed literature and supporting resources are publicly released. Our work establishes a foundational theory, provides actionable evaluation tools, and outlines collaborative pathways to bridge the gap between laboratory research and industrial adoption of triage technologies.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

As modern software systems continue to grow in complexity, triage has become a fundamental process in system operations and maintenance. Triage aims to efficiently prioritize, assign, and assess issues to ensure the reliability of complex environments. The vast amount of heterogeneous data generated by software systems has made effective triage indispensable for maintaining reliability, facilitating maintainability, and enabling rapid issue response. Motivated by these challenges, researchers have devoted extensive effort to advancing triage automation and have achieved significant progress over the past two decades. This survey provides a comprehensive review of 234 papers from 2004 to the present, offering an in-depth examination of the fundamental concepts, system architecture, and problem statement. By comparing the distinct goals of academic and industrial research and by analyzing empirical studies of industrial practices, we identify the major obstacles that limit the practical deployment of triage systems. To assist practitioners in method selection and performance evaluation, we summarize widely adopted open-source datasets and evaluation metrics, providing a unified perspective on the measurement of triage effectiveness. Finally, we outline potential future directions and emerging opportunities to foster a closer integration between academic innovation and industrial application. All reviewed papers and projects are available at https://github.com/AIOps-Lab-NKU/TriageSurvey.

Problem

• Prioritizing and assigning issues in complex software systems efficiently
• Addressing obstacles limiting practical deployment of triage systems
• Bridging the gap between academic innovation and industrial application

Innovation

• Systematic review of software engineering triage research
• Analyzes system architecture and problem statements comprehensively
• Summarizes open datasets and evaluation metrics