Using Process Mining to Generate AI Agents from Software Engineering Process Records
This work provides a method to automatically derive AI agent roles and specifications from software engineering process data, which could help tailor multi-agent systems to specific projects, but the results are preliminary and the approach is incremental.
The paper addresses the challenge of specifying AI agents for hybrid human-AI software engineering teams by combining object-centric, imperative, and declarative process mining to discover project-specific agent roles from repository event logs. As a proof-of-concept on an open-source project, they generated agent specifications and implementations, and validated alignment with human expectations through functional tests and an exploratory user study.
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.