Using Process Mining to Generate AI Agents from Software Engineering Process Records

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.

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

arXiv:2607.04948 [cs.SE]

(or arXiv:2607.04948v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2607.04948 Focus to learn more arXiv-issued DOI via DataCite

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