Ashish Mishra

Test input generators are an important part of property-based testing (PBT) frameworks. Because PBT is intended to test deep semantic and structural properties of a program, the outputs produced by these generators can be complex data structures, constrained to satisfy properties the developer believes is most relevant to testing the function of interest. An important feature expected of these generators is that they be capable of producing all acceptable elements that satisfy the function’s input type and generator-provided constraints. However, it is not readily apparent how we might validate whether a particular generator’s output satisfies this coverage requirement. Typically, developers must rely on manual inspection and post-mortem analysis of test runs to determine if the generator is providing sufficient coverage; these approaches are error-prone and difficult to scale as generators become more complex. To address this important concern, we present a new refinement type-based verification procedure for validating the coverage provided by input test generators, based on a novel interpretation of types that embeds “must-style” underapproximate reasoning principles as a fundamental part of the type system. The types associated with expressions now capture the set of values guaranteed to be produced by the expression, rather than the typical formulation that uses types to represent the set of values an expression may produce. Beyond formalizing the notion of coverage types in the context of a rich core language with higher-order procedures and inductive datatypes, we also present a detailed evaluation study to justify the utility of our ideas.

Component-based synthesis seeks to build programs using the APIs provided by a set of libraries. Oftentimes, these APIs have effects, which make it challenging to reason about the correctness of potential synthesis candidates. This is because changes to global state made by effectful library procedures affect how they may be composed together, yielding an intractably large search space that can confound typical enumerative synthesis techniques. If the nature of these effects are exposed as part of their specification, however, deductive synthesis approaches can be used to help guide the search for components. In this paper, we present a new specification-guided synthesis procedure that uses Hoare-style pre- and post-conditions to express fine-grained effects of potential library component candidates to drive a bi-directional synthesis search strategy. The procedure alternates between a forward search process that seeks to build larger terms given an existing context but which is otherwise unaware of the actual goal, alongside a backward search mechanism that seeks terms consistent with the desired goal but which is otherwise unaware of the context from which these terms must be synthesized. To further improve efficiency and scalability, we integrate a conflict-driven learning procedure into the synthesis algorithm that provides a semantic characterization of previously encountered unsuccessful search paths that is used to prune the space of possible candidates as synthesis proceeds. We have implemented our ideas in a tool called and demonstrate its effectiveness on a number of challenging synthesis problems defined over OCaml libraries equipped with effectful specifications.