Removing Algebraic Data Types from Constrained Horn Clauses Using Difference Predicates
April 16, 2020 Β· Declared Dead Β· π International Joint Conference on Automated Reasoning
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Emanuele De Angelis, Fabio Fioravanti, Alberto Pettorossi, Maurizio Proietti
arXiv ID
2004.07749
Category
cs.PL: Programming Languages
Cross-listed
cs.LO
Citations
11
Venue
International Joint Conference on Automated Reasoning
Last Checked
3 months ago
Abstract
We address the problem of proving the satisfiability of Constrained Horn Clauses (CHCs) with Algebraic Data Types (ADTs), such as lists and trees. We propose a new technique for transforming CHCs with ADTs into CHCs where predicates are defined over basic types, such as integers and booleans, only. Thus, our technique avoids the explicit use of inductive proof rules during satisfiability proofs. The main extension over previous techniques for ADT removal is a new transformation rule, called differential replacement, which allows us to introduce auxiliary predicates corresponding to the lemmas that are often needed when making inductive proofs. We present an algorithm that uses the new rule, together with the traditional folding/unfolding transformation rules, for the automatic removal of ADTs. We prove that if the set of the transformed clauses is satisfiable, then so is the set of the original clauses. By an experimental evaluation, we show that the use of the differential replacement rule significantly improves the effectiveness of ADT removal, and we show that our transformation-based approach is competitive with respect to a well-established technique that extends the CVC4 solver with induction.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted