发表机构
Hasso Plattner Institute (HPI), University of Potsdam; IBM Software Innovation Lab (SIL); University of Haifa; paluno Institute, University of Duisburg Essen（波茨坦大学哈索·普拉特纳研究院（HPI）; IBM软件创新实验室（SIL）; 海法大学; 杜伊斯堡-埃森大学paluno研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何在软件工程中结合人工智能代理，面临代理粒度和关注点分离难题。通过过程挖掘，用软件库事件日志发现特定项目代理角色，生成规范与实现，并进行测试和用户研究验证效果。
AI 中文摘要
将人工智能代理集成到软件工程中面临重大挑战：如何在混合软件工程团队中指定并实现能与人类有效协作的人工智能代理。确定此类代理的正确粒度和关注点分离并非易事。粗粒度代理可能引入难以管理的复杂性，而微代理可能产生严重的协调开销。此外，现有的多代理软件工程框架通常依赖预定义的角色结构，未考虑项目特定特征或过程适应性。我们通过结合以对象为中心、命令式和声明式过程挖掘来解决这一问题。使用从软件存储库中提取的事件日志，我们的方法使用基于存储库行为的预定义软件工程角色词汇表发现特定项目的代理角色，并生成匹配的代理规范和实现。作为概念验证，我们将我们的方法应用于一个成熟的开源项目。我们进行了功能测试和探索性用户研究，以确定生成的人工智能代理规范与人类期望的匹配程度。
英文摘要
Integrating AI agents into Software Engineering (SE) raises an important challenge: how can we specify and realize AI agents that work effectively alongside humans in hybrid SE teams? Determining the right granularity and separation of concerns for such agents is non-trivial. Coarse-grained agents may introduce unmanageable complexity, whereas micro-agents may create severe coordination overhead. Moreover, existing multi-agent SE frameworks typically rely on predefined role structures and do not account for project-specific characteristics or process adaptations. We address this by combining object-centric, imperative, and declarative process mining. Using event logs extracted from software repositories, our approach discovers project-specific agent roles using a predefined SE role vocabulary grounded in repository behavior and generates matching agent specifications and implementations. As proof-of-concept, we applied our approach to a well-established open-source project. We performed functional tests and an exploratory user study to determine how well the generated AI agent specifications are aligned with human expectations.
CommentsTo be published at the 24th International Conference on Business Process Management (BPM 2026), Process Technology Forum
