Retrofitting Symbolic Holes to LLVM IR
June 10, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Bruce Collie, Michael O'Boyle
arXiv ID
2006.05875
Category
cs.PL: Programming Languages
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Symbolic holes are one of the fundamental building blocks of solver-aided and interactive programming. Unknown values can be soundly integrated into programs, and automated tools such as SAT solvers can be used to prove properties of programs containing them. However, supporting symbolic holes in a programming language is challenging; specifying interactions of holes with the type system and execution semantics requires careful design. This paper motivates and introduces the implementation of symbolic holes with unknown type to LLVM IR, a strongly-typed compiler intermediate language. We describe how such holes can be implemented safely by abstracting unsound and type-unsafe details behind a new primitive IR manipulation. Our implementation co-operates well with existing features such as type and dependency checking. Finally, we highlight potentially fruitful areas for investigation using our implementation.
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