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Software developers increasingly rely on automated methods to assess the correctness of their code. One such method is property-based testing (PBT), wherein a test harness generates hundreds or thousands of inputs and checks the outputs of the program on those inputs using parametric properties. Though powerful, PBT induces a sizable gulf of evaluation: developers need to put in nontrivial effort to understand how well the different test inputs exercise the software under test. To bridge this gulf, we propose Tyche, a user interface that supports sensemaking around the effectiveness of property-based tests. Guided by a formative design exploration, our design of Tyche supports developers with interactive, configurable views of test behavior with tight integrations into modern developer testing workflow. These views help developers explore global testing behavior and individual test inputs alike. To accelerate the development of powerful, interactive PBT tools, we define a standard for PBT test reporting and integrate it with a widely used PBT library. A self-guided online usability study revealed that Tyche’s visualizations help developers to more accurately assess software testing effectiveness. 

Random data generators can be thought of as parsers of streams of randomness. This perspective on generators for random data structures is established folklore in the programming languages community, but it has never been formalized, nor have its consequences been deeply explored. We build on the idea of freer monads to develop free generators, which unify parsing and generation using a common structure that makes the relationship between the two concepts precise. Free generators lead naturally to a proof that a monadic generator can be factored into a parser plus a distribution over choice sequences. Free generators also support a notion of derivative, analogous to the familiar Brzozowski derivatives of formal languages, allowing analysis tools to preview the effect of a particular generator choice. This gives rise to a novel algorithm for generating data structures satisfying user-specified preconditions. 

Property-based testing is a mainstay of functional programming, boasting a rich literature, an enthusiastic user community, and an abundance of tools — so many, indeed, that new users may have difficulty choosing. Moreover, any given framework may support a variety of strategies for generating test inputs; even experienced users may wonder which are better in a given situation. Sadly, the PBT literature, though long on creativity, is short on rigorous comparisons to help answer such questions. We present Etna, a platform for empirical evaluation and comparison of PBT techniques. Etna incorporates a number of popular PBT frameworks and testing workloads from the literature, and its extensible architecture makes adding new ones easy, while handling the technical drudgery of performance measurement. To illustrate its benefits, we use Etna to carry out several experiments with popular PBT approaches in both Coq and Haskell, allowing users to more clearly understand best practices and tradeoffs.