This work uses event logs extracted from software repositories to discover project-specific agent roles using a predefined SE role vocabulary grounded in repository behavior and generates matching agent specifications and implementations that are aligned with human expectations.
Abstract
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.
A reproducible, privacy-preserving toolkit and taxonomy that classify human turns and flag cross-cutting consistency work, agent corrections, and retracted requests, and a reproducible, privacy-preserving toolkit and taxonomy that contribute to responsibly engineering EM tooling with AI.
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.
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