Context-Updates Analysis and Refinement in Chisel
September 20, 2017 Β· Declared Dead Β· π SPIN
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Irina Mariuca Asavoae, Mihail Asavoae, Adrian Riesco
arXiv ID
1709.06897
Category
cs.PL: Programming Languages
Cross-listed
cs.LO
Citations
1
Venue
SPIN
Last Checked
4 months ago
Abstract
This paper presents the context-updates synthesis component of Chisel--a tool that synthesizes a program slicer directly from a given algebraic specification of a programming language operational semantics. (By context-updates we understand programming language constructs such as goto instructions or function calls.) The context-updates synthesis follows two directions: an overapproximating phase that extracts a set of potential context-update constructs and an underapproximating phase that refines the results of the first step by testing the behaviour of the context-updates constructs produced at the previous phase. We use two experimental semantics that cover two types of language paradigms: high-level imperative and low-level assembly languages and we conduct the tests on standard benchmarks used in avionics.
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