This paper proposes a novel method for automatically inferring message flow specifications from the communication traces of a system-on-chip (SoC) design that captures messages exchanged among the components during a system execution. The inferred message flows characterize the communication and coordination of components in a system design for realizing various system functions, and they are essential for SoC validation and debugging. The proposed method relieves the burden of manual development and maintenance of such specifications on human designers. Our method also uses a new accuracy metric, \emph{acceptance ratio}, to evaluate the quality of the mined specifications instead of the specification size often used in the previous work, enabling more accurate specifications to be mined. Furthermore, this paper introduces the concept of essential causalities to enhance the accuracy of the message flow mining and accelerate the mining process. The effectiveness of the proposed method is evaluated on both synthetic traces and traces generated from executing several system models in GEM5. In both cases, the proposed method achieves superior accuracies compared to a previous approach. Additionally, this paper includes some practical use cases.

翻译：本文提出了一种新方法，能够从片上系统（SoC）设计的通信轨迹中自动推断消息流规范，该轨迹捕获了系统执行过程中组件间交换的消息。推断出的消息流描述了系统设计中为实现各种系统功能而进行的组件通信与协调，对于SoC验证与调试至关重要。该方法减轻了人工开发与维护此类规范的负担。本文还采用了一种新的精度度量——**接受率**（acceptance ratio），取代以往工作中常使用的规范规模来评估挖掘所得规范的质量，从而能够挖掘出更精确的规范。此外，本文引入了因果必然性（essential causalities）的概念，以提升消息流挖掘的精度并加速挖掘过程。通过在合成轨迹以及使用GEM5执行多个系统模型生成的轨迹上进行评估，该方法均取得了优于先前方法的精度。最后，本文还包含了一些实际应用案例。