Name
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 nonlinear 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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3276501 | Proceedings of the ACM on Programming Languages |
Keywords | DocType | Volume |
Constrained Horn Clauses,Decision Trees,ICE Learning,Software Verification | Journal | 2 |
Issue | ISSN | Citations |
OOPSLA | 2475-1421 | 5 |
PageRank | References | Authors |
0.41 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Ezudheen | 1 | 5 | 0.41 |
| Daniel Neider | 2 | 153 | 21.97 |
| Deepak D'souza | 3 | 239 | 17.90 |
| Pranav Garg 0001 | 4 | 55 | 3.36 |
| Parthasarathy Madhusudan | 5 | 213 | 10.75 |