Horn-ice learning for synthesizing invariants and contracts

Ezudheen, P and Neider, D and D'Souza, D and Garg, P and Madhusudan, P (2018) Horn-ice learning for synthesizing invariants and contracts. In: Proceedings of the ACM on Programming Languages, 2 (OOPSLA).

PDF pro_acm_pro_lan_02_2018 - Published Version Download (438kB)

Abstract

We design learning algorithms for synthesizing invariants using Horn implication counterexamples (Horn-ICE), extending the ICE learning model. In particular, we describe a decision tree learning algorithm that learns from non-linear Horn-ICE samples, works in polynomial time, and uses statistical heuristics to learn small trees that satisfy the samples. Since most verification proofs can be modeled using non-linear Horn clauses, Horn-ICE learning is a more robust technique to learn inductive annotations that prove programs correct. Our experiments show that an implementation of our algorithm is able to learn adequate inductive invariants and contracts efficiently for a variety of sequential and concurrent programs. © 2018 Copyright held by the owner/author(s).

Item Type:	Journal Article
Publication:	Proceedings of the ACM on Programming Languages
Publisher:	Association for Computing Machinery
Additional Information:	The copyright for this article belongs to Authors
Keywords:	Decision trees; Learning systems; Logic programming; Polynomial approximation; Random forests; Trees (mathematics); Verification, Concurrent program; Decision tree learning algorithm; Horn clause; Ice samples; Learning models; Polynomial-time; Robust technique; Software verification, Ice
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	13 Dec 2021 11:42
Last Modified:	13 Dec 2021 11:42
URI:	http://eprints.iisc.ac.in/id/eprint/70786

Actions (login required)

View Item