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Compositional and Lightweight Dependent Type Inference for ML

机译:ML的组成和轻质依赖性型推理

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We consider the problem of inferring expressive safety properties of higher-order functional programs using first-order decision procedures. Our approach encodes higher-order features into first-order logic formula whose solution can be derived using a lightweight counterexample guided refinement loop. To do so, we extract initial verification conditions from dependent typing rules derived by a syntactic scan of the program. Subsequent type-checking and type-refinement phases infer and propagate specifications of higher order functions, which are treated as uninterpreted first-order constructs, via subtyping chains. Our technique provides several benefits not found in existing systems: (1) it enables compositional verification and inference of useful safety properties for functional programs; (2) additionally provides counterexamples that serve as witnesses of unsound assertions: (3) does not entail a complex translation or encoding of the original source program into a first-order representation; and, (4) most importantly, profitably employs the large body of existing work on verification of first-order imperative programs to enable efficient analysis of higher-order ones. We have implemented the technique as part of the MLton SML compiler toolchain, where it has shown to be effective in discovering useful invariants with low annotation burden.
机译:我们考虑使用一阶决策程序来考虑推断高阶功能计划的表达安全性质的问题。我们的方法将更高阶的功能编码为一阶逻辑公式,其解决方案可以使用轻量级的反例引导改进循环导出。为此,我们从通过程序的句法扫描派生的依赖键入规则中提取初始验证条件。随后的类型检查和类型细化阶段推断和传播更高阶函数的规范,通过亚级链被视为未解释的一阶结构。我们的技术提供了现有系统中未发现的几个好处:(1)它可以实现功能计划的有用安全性能的组成验证和推理; (2)另外,提供了作为非破坏断言的证人的反例:(3)不需要复杂的翻译或将原始源程序的编码成一阶表示; (4)最重要的是,有利可图地雇用了大量现有工作,以验证一阶的势命计划,以便能够有效地分析高阶。我们已经实施了该技术作为MLTON SML编译器工具链的一部分,在那里它表明有效地发现具有低注释负担的有用不变性。

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