Property-based testing (PBT) is a technique for validating code against an executable specification by automatically generating test-data. We present a proof-theoretical reconstruction of this style of testing for relational specifications and employ the Foundational Proof Certificate framework to describe test generators. We do this by presenting certain kinds of "proof outlines" that can be used to describe various common generation strategies in the PBT literature, ranging from random to exhaustive, including their combination. We also address the shrinking of counterexamples as a first step towards their explanation. Once generation is accomplished, the testing phase boils down to a standard logic programming search. After illustrating our techniques on simple, first-order (algebraic) data structures, we lift it to data structures containing bindings using λ-tree syntax. The λProlog programming language is capable of performing both the generation and checking of tests. We validate this approach by tackling benchmarks in the metatheory of programming languages coming from related tools such as PLT-Redex.
Property-Based Testing via Proof Reconstruction / R. Blanco, D. Miller, A. Momigliano - In: PPDP '19 : Proceedings[s.l] : ACM, 2019. - ISBN 9781450372497. - pp. 1-13 (( Intervento presentato al 21. convegno International Symposium on Principles and Practice of Declarative Programming (PPDP) tenutosi a Porto nel 2019 [10.1145/3354166.3354170].
Property-Based Testing via Proof Reconstruction
A. Momigliano
2019
Abstract
Property-based testing (PBT) is a technique for validating code against an executable specification by automatically generating test-data. We present a proof-theoretical reconstruction of this style of testing for relational specifications and employ the Foundational Proof Certificate framework to describe test generators. We do this by presenting certain kinds of "proof outlines" that can be used to describe various common generation strategies in the PBT literature, ranging from random to exhaustive, including their combination. We also address the shrinking of counterexamples as a first step towards their explanation. Once generation is accomplished, the testing phase boils down to a standard logic programming search. After illustrating our techniques on simple, first-order (algebraic) data structures, we lift it to data structures containing bindings using λ-tree syntax. The λProlog programming language is capable of performing both the generation and checking of tests. We validate this approach by tackling benchmarks in the metatheory of programming languages coming from related tools such as PLT-Redex.File | Dimensione | Formato | |
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