Efficient join order for constraint automata through LLM-generated heuristics

Reo is an exogenous coordination language designed for component-based systems based on channel-based connectors. Constraint automata is defined by Christel Baier et al. as the compositional operational semantics of Reo. Semantics of a Reo circuit is computed by joining the constraint automata of the connector elements. This computation can be costly when dealing with large connectors, making an improvement necessary. Improving this operation involves either improving the join algorithm or selecting a joining order that minimizes intermediate automata. While alternative algorithms for joining constraint automata have been proposed, identifying an efficient joining order remains a challenge. This paper proposes a heuristic-based approach for finding an efficient order of joining constraint automata. By feeding OpenAI’s ChatGPT with data on the join algorithm and the structure of constraint automata, we ask it to generate diverse heuristics to identify the most efficient joining order and employ its suggestions. Our results demonstrate the impact of join order on the operation’s performance. We analyze these results to identify the best heuristic for each set of CAs based on their characteristics. This highlights the potential of LLM-driven approaches in assisting the development of efficient solutions for computational tasks.

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