The foundation of hardware verification is that an implementation satisfies its specification. The specification described by a natural language may be ambiguous and inconsistence. The Acclellera Property Specification Language (PSL) is developed for the formal specification of hardware. It provides a standard means of specifying design properties using a concise syntax with clearly-defined formal semantics, particularly it offers an input method for dynamic(simulation) and static(formal verification method, such as model checking) verification tools. This paper proposes how to use model checking to verify a property with "X" value, and gives a model checking algorithm based on 3-valued (true, false, X) logic formula of PSL. This algorithm has the same time complexity as 2-valued logic model checking. Finally, we present how to separate CTL formula from a PSL verification unit, and verify these properties from PSL under the given model.

Programming by Example (PBE) is a program synthesis paradigm in which the synthesizer creates a program that matches a set of given examples. In many applications of such synthesis (e.g., program repair or reverse engineering), we are to reconstruct a program that is close to a specific target program, not merely to produce some program that satisfies the seen examples. In such settings, we wish that the synthesized program generalizes well, i.e., has as few errors as possible on the unobserved examples capturing the target function behavior. In this paper, we propose the first framework (called SynGuar) for PBE synthesizers that guarantees to achieve low generalization error with high probability. Our main contribution is a procedure to dynamically calculate how many additional examples suffice to theoretically guarantee generalization. We show how our techniques can be used in 2 well-known synthesis approaches: PROSE and STUN (synthesis through unification), for common string-manipulation program benchmarks. We find that often a few hundred examples suffice to provably bound generalization error below 5% with high (≥ 98%) probability on these benchmarks. Further, we confirm this empirically: SynGuar significantly improves the accuracy of existing synthesizers in generating the right target programs. But with fewer examples chosen arbitrarily, the same baseline synthesizers (without SynGuar) overfit and lose accuracy. CCS CONCEPTS• Software and its engineering → Software notations and tools; General programming languages.

A transpiler converts code from one programming language to another. Many practical uses of transpilers require the user to be able to guide or customize the program produced from a given input program. This customizability is important for satisfying many application-specific goals for the produced code such as ensuring performance, readability, ease of exposition or maintainability, compatibility with external environment or analysis tools, and so on. Conventional transpilers are deterministic rule-driven systems often written without offering customizability per user and per program. Recent advances in transpilers based on neural networks offer some customizability to users, e.g. through interactive prompts, but they are still difficult to precisely control the production of a desired output. Both conventional and neural transpilation also suffer from the "last mile" problem: they produce correct code on average, i.e., on most parts of a given program, but not necessarily for all parts of it. We propose a new transpilation approach that offers fine-grained customizability and reusability of transpilation rules created by others, without burdening the user to understand the global semantics of the given source program. Our approach is mostly automatic and incremental, i.e., constructs translation rules needed to transpile the given program as per the user's guidance piece-by-piece. Users can rely on existing transpilation rules to translate most of the program correctly while focusing their effort locally, only on parts that are incorrect or need customization. This improves the correctness of the end result. We implement the transpiler as a tool called DuoGlot, which translates Python to Javascript programs, and evaluate it on the popular GeeksForGeeks benchmarks. DuoGlot achieves 90% translation accuracy and so it outperforms all existing translators (both handcrafted and neural-based), while it produces readable code. We evaluate DuoGlot on two additional benchmarks, containing more challenging and longer programs, and similarly observe improved accuracy compared to the other transpilers.

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