Accelerating Reinforcement Learning-Based CCSL Specification Synthesis Using Curiosity-Driven Exploration

Abstract

The Clock Constraint Specification Language (CCSL) has been widely acknowledged as a promising system-level specification for the modeling and analysis of timing behaviors of real-time and embedded systems. However, along with the increasing complexity of modern systems coupled with strict time-to-market constraints, it becomes more and more difficult for requirement engineers to accurately figure out CCSL specifications from natural language-based requirement documents, since they lack both expertise in formal CCSL modeling and design automation tools to support quick and automatic generation of CCSL specifications. To solve the above problem, in this paper we introduce a novel and efficient Reinforcement Learning (RL)-based synthesis approach that can facilitate requirement engineers to quickly figure out their expected CCSL specifications. For a given incomplete CCSL specification, our approach adopts RL-based enumeration to explore all the feasible solutions to fill the holes within CCSL constraints, and leverages curiosity-driven exploration to accelerate the enumeration process. Based on the combination of our proposed curiosity-driven exploration heuristic and deductive reasoning techniques, our approach can not only prune unfruitful enumeration solutions effectively, but also optimize the enumeration process to search for the tightest solution quickly, thus the overall synthesis process can be accelerated dramatically. Comprehensive experimental results demonstrate that our approach significantly outperforms state-of-the-art methods in terms of both synthesis time and synthesis accuracy.