Direct Interpretation of Functional Programs for Debugging
May 16, 2019 Β· Declared Dead Β· π ML/OCaml
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
John Whitington, Tom Ridge
arXiv ID
1905.06545
Category
cs.PL: Programming Languages
Cross-listed
cs.SE
Citations
4
Venue
ML/OCaml
Last Checked
3 months ago
Abstract
We make another assault on the longstanding problem of debugging. After exploring why debuggers are not used as widely as one might expect, especially in functional programming environments, we define the characteristics of a debugger which make it usable and thus widely used. We present initial work towards a new debugger for OCaml which operates by direct interpretation of the program source, allowing the printing out of individual steps of the program's evaluation. We present OCamli, a standalone interpreter, and propose a mechanism by which the interpreter could be integrated into compiled executables, allowing part of a program to be interpreted in the same fashion as OCamli whilst the rest of the program runs natively. We show how such a mechanism might create a source-level debugging system that has the characteristics of a usable debugger (such as being independent of its environment) and so may eventually be expected to be suitable for widespread adoption.
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