Background: Systems of systems are becoming increasingly dynamic and heterogeneous, and this adds pressure on the long-standing challenge of interoperability. Besides its technical aspect, interoperability has also an economic side, as development time efforts are required to build the interoperability artifacts. Objectives: With the recent advances in the field of large language models (LLMs), we aim at analyzing the effectiveness of LLM-based strategies to make systems interoperate autonomously, at runtime, without human intervention. Method: We selected 13 open source LLMs and curated four versions of a dataset in the agricultural interoperability use case. We performed three runs of each model with each version of the dataset, using two different strategies. Then we compared the effectiveness of the models and the consistency of their results across multiple runs. Results: qwen2.5-coder:32b was the most effective model using both strategies DIRECT (average pass@1 >= 0.99) and CODEGEN (average pass@1 >= 0.89) in three out of four dataset versions. In the fourth dataset version, which included an unit conversion, all models using the strategy DIRECT failed, whereas using CODEGEN qwen2.5-coder:32b succeeded with an average pass@1 = 0.75. Conclusion: Some LLMs can make systems interoperate autonomously. Further evaluation in different domains is recommended, and further research on reliability strategies should be conducted.

翻译：背景：系统之系统正变得日益动态化和异构化，这加剧了长期存在的互操作性挑战。除了技术层面，互操作性还具有经济层面的考量，因为构建互操作性构件需要投入开发时间。目标：随着大语言模型领域的最新进展，我们旨在分析基于LLM的策略在无需人工干预的情况下，使系统在运行时自主实现互操作的有效性。方法：我们选取了13个开源LLM，并在农业互操作性用例中构建了四个版本的数据集。我们使用两种不同策略，对每个模型在每个数据集版本上进行了三轮运行。随后比较了各模型的有效性及其在多轮运行中结果的一致性。结果：在四个数据集版本中的三个版本中，qwen2.5-coder:32b在使用DIRECT（平均pass@1 >= 0.99）和CODEGEN（平均pass@1 >= 0.89）两种策略时均表现出最高有效性。在包含单位转换的第四个数据集版本中，所有使用DIRECT策略的模型均失败，而使用CODEGEN策略的qwen2.5-coder:32b则以平均pass@1 = 0.75取得成功。结论：部分LLM能够使系统实现自主互操作。建议在不同领域进行进一步评估，并开展关于可靠性策略的深入研究。