Debugging Functional Programs by Interpretation
November 01, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
John Whitington
arXiv ID
2411.00637
Category
cs.PL: Programming Languages
Citations
4
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Motivated by experience in programming and in the teaching of programming, we make another assault on the longstanding problem of debugging. Having explored 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 likely to be widely used. We present work on a new debugger for the functional programming language OCaml which operates by direct interpretation of the program source, allowing the printing out of individual steps of the program's evaluation, and discuss its technical implementation and practical use. It has two parts: a stand-alone debugger which can run OCaml programs by interpretation and so allow their behaviour to be inspected; and an OCaml syntax extension, which allows the part of a program under scrutiny to be interpreted in the same fashion as the stand-alone debugger whilst the rest of the program runs natively. We show how this latter mechanism can create a source-level debugging system that has the characteristics of a usable debugger 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