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The paper reframes software remodularization as a distributed consensus problem, using autonomous agents to negotiate decompositions based on cohesion and stability. They introduce an Asymmetric Monotonic Concession Protocol (AMCP) that allows agents to negotiate decompositions respecting multi-attribute utility thresholds. The protocol is formally proven to terminate and produce locally Pareto-satisfactory partitions, and experiments show it matches state-of-the-art optimizers while enforcing stability constraints.
Guaranteeing software stability during remodularization doesn't require sacrificing performance; a multi-agent consensus protocol can match state-of-the-art optimizers while acting as a "circuit breaker" for strict stability constraints.
Automatic software remodularisation is typically cast as a single-objective optimization problem. While recent metaheuristics have improved search efficiency, real-world architecture recovery must reconcile the conflicting attributes of structural cohesion and evolutionary stability. We reframe software module clustering as a distributed consensus problem among autonomous agents. We introduce an Asymmetric Monotonic Concession Protocol (AMCP) that enables agents to negotiate decompositions that respect multi-attribute utility thresholds. We formally prove the protocol's termination, its bounded concession behaviour consistent with the Zeuthen Strategy under closed-instance conditions, and the local Pareto-satisfactoriness of the resulting partitions. Preliminary experiments on a synthetic benchmark and the Xwork Java framework confirm that our negotiated consensus matches state-of-the-art optimizers when stability budgets are loose, while acting as a"circuit breaker"to enforce strict stability constraints. Extended results on ten further systems, including comparisons with multi-objective evolutionary algorithms and multi-version chains, will be reported in a forthcoming full paper.
