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Enumeration and Deduction Driven Co-Synthesis of CCSL Specifications Using Reinforcement Learning

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Abstract

The Clock Constraint Specification Language (CCSL) has become popular for modeling and analyzing timing behaviors of real-time embedded systems. However, it is difficult for requirement engineers to accurately figure out CCSL specifications from natural language-based requirement descriptions. This is mainly because: i) most requirement engineers lack expertise in formal modeling; and ii) few existing tools can be used to facilitate the generation of CCSL specifications. To address these issues, this paper presents a novel approach that combines the merits of both Reinforcement Learning (RL) and deductive techniques in logical reasoning for efficient co-synthesis of CCSL specifications. Specifically, our method leverages RL to enumerate all the feasible solutions to fill the holes of incomplete specifications and deductive techniques to judge the quality of each trial. Our proposed deductive mechanisms are useful for not only pruning enumeration space, but also guiding the enumeration process to reach an optimal solution quickly. Comprehensive experimental results on both well-known benchmarks and complex industrial examples demonstrate the performance and scalability of our method. Compared with the state-of-theart, our approach can drastically reduce the synthesis time by several orders of magnitude while the accuracy of synthesis can be guaranteed.
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Dates and versions

hal-03525306 , version 1 (13-01-2022)

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Ming Hu, Jiepin Ding, Min Zhang, Frédéric Mallet, Mingsong Chen. Enumeration and Deduction Driven Co-Synthesis of CCSL Specifications Using Reinforcement Learning. RTSS 2021 - IEEE Real-Time Systems Symposium, Dec 2021, Dortmund / Virtual, Germany. pp.227-239, ⟨10.1109/RTSS52674.2021.00030⟩. ⟨hal-03525306⟩
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