Reasoning about the correctness of distributed systems is a significant challenge, with precise correctness specifications serving as an essential prerequisite to verification. However, identifying and formulating specifications remains a major hurdle for developers in practice. Specy addresses this challenge by automatically learning specifications from observable event traces generated by message exchanges in distributed systems. The system employs a specialized grammar tailored for event-based specifications, incorporating support for quantifiers over events - a capability essential for capturing the complex behavioral patterns inherent in distributed protocols. Specy utilizes a novel learning procedure that combines grammar-based enumerative search with dynamic learning from event traces, providing effective control over the specification search. We evaluated Specy on established distributed protocols and industrial case studies, demonstrating its ability to successfully learn important protocol specifications. Specy can discover previously unidentified specifications overlooked by developers, automatically derive inductive invariants that were previously constructed manually for verification purposes, and, through run-time monitoring in production systems, reveal gaps in testing coverage - highlighting opportunities to leverage specifications in practice.

Ideally, accelerator development should be as easy as software development. Several recent design languages/tools are working toward this goal, but actually testing early designs on real applications end-to-end remains prohibitively difficult due to the costs of building specialized compiler and simulator support. We propose a new first-in-class, mostly automated methodology termed “3LA” to enable end-to-end testing of prototype accelerator designs on unmodified source applications. A key contribution of 3LA is the use of a formal software/hardware interface that specifies an accelerator’s operations and their semantics. Specifically, we leverage the Instruction-level Abstraction (ILA) formal specification for accelerators that has been successfully used thus far for accelerator implementation verification. We show how the ILA for accelerators serves as a software/hardware interface, similar to the Instruction Set Architecture for processors, that can be used for automated development of compilers and instruction-level simulators. Another key contribution of this work is to show how ILA-based accelerator semantics enables extending recent work on equality saturation to auto-generate basic compiler support for prototype accelerators in a technique we term “flexible matching.” By combining flexible matching with simulators auto-generated from ILA specifications, our approach enables end-to-end evaluation with modest engineering effort. We detail several case studies of 3LA, which uncovered an unknown flaw in a recently published accelerator and facilitated its fix.