Jul 2026· Fall Joint Computer Conference· pp. 420-425· 0 citations· 18 references
Abstract
AI agent systems increasingly support software engineering by extending large language models with capabilities such as planning, tool use, and coordinated execution, yet empirical evidence on the engineering challenges of building and maintaining such frameworks remains limited. To fill this gap, we conduct a large-scale empirical study of 3,864 closed GitHub issues from three representative repositories. We present a taxonomy of engineering challenges comprising 5 top-level categories and 21 subcategories, analyze the popularity and difficulty of these categories, and summarize 47 actionable solution strategies from resolved issue discussions and linked pull requests. These findings provide practical guidance for developers and framework providers, and offer an empirical basis for future research on AI agent engineering.
A comprehensive overview of the existing tools and frameworks for implementing MAS in software engineering and a set of lessons learned and challenges that can help researchers and practitioners to select a suitable MAS framework according to their needs are provided.
Maria Sâmyla Serafim de Oliveira, M. Ibiyo, Marco Gianrusso et al.· 0 citations
Overall, repository-preserved Agent Plans under these tool-specific directories appear to be a narrow but informative artifact for studying task intent and execution guidance in human-agent workflows.
M. Abubakar, Seyedmoein Mohsenimofidi, Jai Lal Lulla et al.· 1 citation
This workshop aims to define a roadmap for a world where AI Teammates and human developers build the future together, anchored by the launch of the AIDev dataset, which provides the empirical evidence needed to understand the behaviors of AI Teammates.
Hao Li, Haoxiang Zhang, Jie M. Zhang et al.· Proceedings of the 32nd ACM...· 1 citation
The integration of Large Language Models (LLMs) into software development workflows has fundamentally transformed how developers interact with knowledge repositories, codebases, and documentation. However, traditional knowledge retrieval mechanisms face significant challenges when applied to the dynamic, multi-modal, and contextually-rich environment of software engineering. This survey provides a comprehensive analysis of knowledge retrieval architectures specifically designed for AI-assisted software development, examining the evolution from static context provision to autonomous development agents. Building upon this systematic examination, the paper delineates critical research directions that advance the theoretical and practical foundations of AI-assisted software development.
Norbert Fijałek, I. Bluemke, Piotr Gawrysiak· WiPiEC Journal - Works in Pr...· 0 citations
Through applied case studies in pharmaceutical discovery and financial systems, common design patterns that make agentic systems successful are analyzed, and practical mitigation strategies for failure modes are discussed, such as verification pipelines, fallback mechanisms, and human-in-the-loop supervision.
Grace Hui Yang, P. Venkit, Hooman Sedghamiz et al.· Proceedings of the 32nd ACM...· 0 citations
GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications. However, this growth has outpaced the maturity of the field. GUI agents remain technically brittle, incompletely engineered, and insufficiently validated for sustained real-world use. They are evolving into closed-loop software systems. Within these systems, model reasoning is coupled with interface perception, execution feedback, recovery, and human oversight. This evolution calls for a software engineering perspective that remains largely absent from existing research. We address this gap by reviewing 336 GUI-agent papers from January 2018 to April 2026. Five research questions examine the research landscape, architectures, evaluation, software lifecycle concerns, and future opportunities. Our findings show that the field has expanded sharply since 2024, while mobile and web settings remain dominant. Architectures increasingly adopt modular perceive-reason-act loops, but recovery, human escalation, safety enforcement, and auditability remain underdeveloped. This architectural imbalance extends to evaluation. Evaluations are becoming more interactive, but they remain centered on task success and are difficult to compare across protocols. More broadly, existing studies provide limited support for testing beyond benchmarks and for maintaining agents after release. Observability, privacy engineering, and systematic human oversight are also underdeveloped. Together, these findings show that capability improvements alone cannot ensure deployment readiness. Future research should connect dependable execution with lifecycle-centered testing and reproducible evaluation. It should also integrate permission and privacy controls with cost-aware, human-centered governance. This integration is necessary to build dependable, maintainable, secure, and deployable GUI-agent systems.
Sheng-Cheng Yu, Yuchen Ling, Junyang Xing et al.· 1 citation
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