Synthesizing Program-Specific Static Analyses
October 15, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Colin S. Gordon
arXiv ID
1810.06600
Category
cs.PL: Programming Languages
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Designing a static analysis is generally a substantial undertaking, requiring significant expertise in both program analysis and the domain of the program analysis, and significant development resources. As a result, most program analyses target properties that are universallly of interest (e.g., absence of null pointer dereference) or nearly so (e.g., deadlock freedom). However, many interesting program properties that would benefit from static checking are specific to individual programs, or sometimes programs utilizing a certain library. It is impractical to devote program analysis and verification experts to these problems. We propose instead to work on example-based synthesis of program analyses within well-understood domains like type qualifier systems and effect systems. The dynamic behaviors behind the classes of problems these systems prevent correspond to examples that developers who lack expertise in static analysis can readily provide (data flow paths, or stack traces).
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