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

• URL: http://arxiv.org/abs/2511.08607v1
• Date: Thu, 13 Nov 2025 01:00:33 GMT
• Title: Triage in Software Engineering: A Systematic Review of Research and Practice
• Authors: Yongxin Zhao, Shenglin Zhang, Yujia Wu, Yuxin Sun, Yongqian Sun, Dan Pei, Chetan Bansal, Minghua Ma,
• Abstract summary: Triage aims to efficiently prioritize, assign, and assess issues to ensure the reliability of complex environments.<n>The vast amount of heterogeneous data generated by software systems has made effective triage indispensable.<n>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.
• Score: 18.03124877437556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• Can AI Generate more Comprehensive Test Scenarios? Review on Automated Driving Systems Test Scenario Generation Methods [19.39586739934126]
  This review systematically analyzes 31 primary studies,and 10 surveys identified through a comprehensive search spanning 20152025.<n>Traditional approaches rely on expert knowledge,ontologies,and naturalistic driving or accident data,while recent developments leverage generative models,including large language models, adversarial networks,diffusion models,and reinforcement learning frameworks,to synthesize diverse and safety-critical scenarios.
  arXiv  Detail & Related papers  (2025-12-17T13:14:15Z)
• Let the Barbarians In: How AI Can Accelerate Systems Performance Research [80.43506848683633]
  We term this iterative cycle of generation, evaluation, and refinement AI-Driven Research for Systems.<n>We demonstrate that ADRS-generated solutions can match or even outperform human state-of-the-art designs.
  arXiv  Detail & Related papers  (2025-12-16T18:51:23Z)
• From Task Executors to Research Partners: Evaluating AI Co-Pilots Through Workflow Integration in Biomedical Research [0.16174969956296248]
  This rapid review examines benchmarking practices for AI systems in preclinical biomedical research.<n>A process-oriented evaluation framework is proposed that addresses four critical dimensions absent from current benchmarks.<n>These dimensions are essential for evaluating AI systems as research co-pilots rather than as isolated task executors.
  arXiv  Detail & Related papers  (2025-12-04T14:37:46Z)
• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks [14.762911285395047]
  We evaluate seven general-purpose agent frameworks across three representative code-centric tasks.<n>Our findings reveal distinct capability patterns and trade-offs among the evaluated frameworks.<n>For overhead, software development incurs the highest monetary cost, while GPTswarm remains the most cost-efficient.
  arXiv  Detail & Related papers  (2025-11-02T09:46:59Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation [0.0]
  Retrieval-Augmented Generation (RAG) is a rapidly evolving field within AI.<n>There is a significant lack of research on its practical application in industrial contexts.<n>Our study investigates how companies apply RAG in practice.
  arXiv  Detail & Related papers  (2025-08-11T09:40:54Z)
• ResearcherBench: Evaluating Deep AI Research Systems on the Frontiers of Scientific Inquiry [22.615102398311432]
  We introduce ResearcherBench, the first benchmark focused on evaluating the capabilities of deep AI research systems.<n>We compiled a dataset of 65 research questions expertly selected from real-world scientific scenarios.<n>OpenAI Deep Research and Gemini Deep Research significantly outperform other systems, with particular strength in open-ended consulting questions.
  arXiv  Detail & Related papers  (2025-07-22T06:51:26Z)
• Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems [92.89673285398521]
  o1-like reasoning systems have demonstrated remarkable capabilities in solving complex reasoning tasks.<n>We introduce an imitate, explore, and self-improve'' framework to train the reasoning model.<n>Our approach achieves competitive performance compared to industry-level reasoning systems.
  arXiv  Detail & Related papers  (2024-12-12T16:20:36Z)
• A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research [23.273934717819795]
  This paper presents a systematic literature review of approaches that aim to improve the explainability of AI models within the context of Software Engineering.<n>We aim to summarize the SE tasks where XAI techniques have shown success to date; (2) classify and analyze different XAI techniques; and (3) investigate existing evaluation approaches.
  arXiv  Detail & Related papers  (2024-01-26T03:20:40Z)
• Scaling up Search Engine Audits: Practical Insights for Algorithm Auditing [68.8204255655161]
  We set up experiments for eight search engines with hundreds of virtual agents placed in different regions. We demonstrate the successful performance of our research infrastructure across multiple data collections. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time.
  arXiv  Detail & Related papers  (2021-06-10T15:49:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.