Thu 8 Nov 2018 10:30 - 10:52 at Studio 2 - Types and Contracts Chair(s): Hakjoo Oh

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 nonlinear 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.

Thu 8 Nov

10:30 - 12:00: OOPSLA - Types and Contracts at Studio 2
Chair(s): Hakjoo OhKorea University
splash-2018-OOPSLA10:30 - 10:52
Deepak D'Souza, Ezudheen P, Pranav GargUniversity of Illinois at Urbana-Champaign, Daniel NeiderMax Planck Institute for Software Systems, P. MadhusudanUniversity of Illinois at Urbana-Champaign
splash-2018-OOPSLA10:52 - 11:15
Niki VazouIMDEA Software Institute, √Čric TanterUniversity of Chile & Inria Paris, David Van HornUniversity of Maryland, USA
splash-2018-OOPSLA11:15 - 11:37
Daniel FelteyNorthwestern University, USA, Ben GreenmanNortheastern University, USA, Christophe ScholliersUniversiteit Gent, Belgium, Robby FindlerNorthwestern University, USA, Vincent St-AmourNorthwestern University
splash-2018-OOPSLA11:37 - 12:00
Jack WilliamsUniversity of Edinburgh, UK, J. Garrett MorrisUniversity of Kansas, USA, Philip WadlerUniversity of Edinburgh, UK