Synthesizing contracts correct modulo a test generator

Abstract

We present an approach to learn contracts for object-oriented programs where guarantees of correctness of the contracts are made with respect to a test generator. Our contract synthesis approach is based on a novel notion of tight contracts and an online learning algorithm that works in tandem with a test generator to synthesize tight contracts. We implement our approach in a tool called Precis and evaluate it on a suite of programs written in C#, studying the safety and strength of the synthesized contracts, and compare them to those synthesized by Daikon.

Languages with Decidable Learning: A Meta-theorem

“…Other recent work studies the parameterized complexity of learning queries in FO [van Bergerem et al 2022], algorithms for learning in FO with counting [van Bergerem 2019], and learning in description logics [Funk et al 2019]. Applications for FO learning have emerged, e.g., synthesizing invariants [Garg et al 2014Hance et al 2021;Koenig et al 2020Koenig et al , 2022Yao et al 2021] and learning program properties [Astorga et al 2019[Astorga et al , 2021Miltner et al 2020].…”

“…We undertake a foundational theoretical exploration of the exact learning problem for symbolic languages with rich semantics. Learning symbolic concepts from data has myriad applications, e.g., in verification [Garg et al 2014Ivanov et al 2021;Neider et al 2020;Zhu et al 2018] and, in particular, invariant synthesis for distributed protocols [Hance et al 2021;Koenig et al 2020Koenig et al , 2022Yao et al 2021], learning properties of programs [Astorga et al 2019[Astorga et al , 2021Miltner et al 2020], explaining executions of distributed protocols [Neider and Gavran 2018], and synthesizing programs from examples or specifications [Alur et al 2015;Evans and Grefenstette 2018;Gulwani 2011;Handa and Rinard 2020;Muggleton et al 2014;Polozov and Gulwani 2015;Solar-Lezama et al 2006;Wang et al 2017a,b].…”