Property-based testing (PBT) relies on generators for random test cases, often constructed using embedded domain specific languages, which provide expressive combinators for building and composing generators. The effectiveness of PBT depends critically on the speed of these generators. However, careful measurements show that the generator performance of widely used PBT libraries falls well short of what is possible, due principally to (1) the abstraction overhead of their combinator-heavy style and (2) suboptimal sources of randomness. We characterize, quantify, and address these bottlenecks. To eliminate abstraction overheads, we propose a technique based on multi-stage programming, dubbed Allegro. We apply this technique to leading generator libraries in OCaml and Scala 3, significantly improving performance. To quantify the performance impact of the randomness source, we carry out a controlled experiment, replacing the randomness in the OCaml PBT library with an optimized version. Both interventions exactly preserve the semantics of generators, enabling precise, pointwise comparisons. Together, these improvements find bugs up to 13× faster.

Rewrite rules are critical in equality saturation, an increasingly popular technique in optimizing compilers, synthesizers, and verifiers. Unfortunately, developing high-quality rulesets is difficult and error-prone. Recent work on automatically inferring rewrite rules does not scale to large terms or grammars, and existing rule inference tools are monolithic and opaque. Equality saturation users therefore struggle to guide inference and incrementally construct rulesets. As a result, most users still manually develop and maintain rulesets. This paper proposes Enumo, a new domain-specific language for programmable theory exploration. Enumo provides a small set of core operators that enable users to strategically guide rule inference and incrementally build rulesets. Short Enumo programs easily replicate results from state-of-the-art tools, but Enumo programs can also scale to infer deeper rules from larger grammars than prior approaches. Its composable operators even facilitate developing new strategies for ruleset inference. We introduce a new fast-forwarding strategy that does not require evaluating terms in the target language, and can thus support domains that were out of scope for prior work. We evaluate Enumo and fast-forwarding across a variety of domains. Compared to state-of-the-art techniques, enumo can synthesize better rulesets over a diverse set of domains, in some cases matching the effects of manually-developed rulesets in systems driven by equality saturation.