A Symbolic Computing Perspective on Software Systems
- URL: http://arxiv.org/abs/2406.09085v1
- Date: Thu, 13 Jun 2024 13:10:47 GMT
- Title: A Symbolic Computing Perspective on Software Systems
- Authors: Arthur C. Norman, Stephen M. Watt,
- Abstract summary: Symbolic mathematical computing systems have served as a canary in the coal mine of software systems for more than sixty years.
All of the major symbolic mathematical computing systems include low-level code for arithmetic, memory management and other primitives, a compiler or interpreter for a bespoke programming language, a library of high level mathematical algorithms, and some form of user interface.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Symbolic mathematical computing systems have served as a canary in the coal mine of software systems for more than sixty years. They have introduced or have been early adopters of programming language ideas such ideas as dynamic memory management, arbitrary precision arithmetic and dependent types. These systems have the feature of being highly complex while at the same time operating in a domain where results are well-defined and clearly verifiable. These software systems span multiple layers of abstraction with concerns ranging from instruction scheduling and cache pressure up to algorithmic complexity of constructions in algebraic geometry. All of the major symbolic mathematical computing systems include low-level code for arithmetic, memory management and other primitives, a compiler or interpreter for a bespoke programming language, a library of high level mathematical algorithms, and some form of user interface. Each of these parts invokes multiple deep issues. We present some lessons learned from this environment and free flowing opinions on topics including: * Portability of software across architectures and decades; * Infrastructure to embrace and infrastructure to avoid; * Choosing base abstractions upon which to build; * How to get the most out of a small code base; * How developments in compilers both to optimise and to validate code have always been and remain of critical importance, with plenty of remaining challenges; * The way in which individuals including in particular Alan Mycroft who has been able to span from hand-crafting Z80 machine code up to the most abstruse high level code analysis techniques are needed, and * Why it is important to teach full-stack thinking to the next generation.
Related papers
- Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
We propose a complex reasoning schema over KG upon large language models (LLMs)
We augment the arbitrary first-order logical queries via binary tree decomposition to stimulate the reasoning capability of LLMs.
Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods.
arXiv Detail & Related papers (2024-05-02T18:12:08Z) - CodeComplex: A Time-Complexity Dataset for Bilingual Source Codes [6.169110187130671]
We introduce CodeComplex, a novel source code dataset where each code is manually annotated with a corresponding worst-case time complexity.
To the best of our knowledge, CodeComplex stands as the most extensive code dataset tailored for predicting complexity.
We present the outcomes of our experiments employing various baseline models, leveraging state-of-the-art neural models in code comprehension.
arXiv Detail & Related papers (2024-01-16T06:54:44Z) - Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit [63.82016263181941]
Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora.
Currently, there is already a thriving research community focusing on code intelligence.
arXiv Detail & Related papers (2023-12-30T17:48:37Z) - Guess & Sketch: Language Model Guided Transpilation [59.02147255276078]
Learned transpilation offers an alternative to manual re-writing and engineering efforts.
Probabilistic neural language models (LMs) produce plausible outputs for every input, but do so at the cost of guaranteed correctness.
Guess & Sketch extracts alignment and confidence information from features of the LM then passes it to a symbolic solver to resolve semantic equivalence.
arXiv Detail & Related papers (2023-09-25T15:42:18Z) - When Do Program-of-Thoughts Work for Reasoning? [51.2699797837818]
We propose complexity-impacted reasoning score (CIRS) to measure correlation between code and reasoning abilities.
Specifically, we use the abstract syntax tree to encode the structural information and calculate logical complexity.
Code will be integrated into the EasyInstruct framework at https://github.com/zjunlp/EasyInstruct.
arXiv Detail & Related papers (2023-08-29T17:22:39Z) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - Deep Distilling: automated code generation using explainable deep
learning [0.0]
We introduce deep distilling, a machine learning method that learns patterns from data using explainable deep learning.
We show that deep distilling generates concise code that generalizes out-of-distribution to solve problems.
Our approach demonstrates that unassisted machine intelligence can build generalizable and intuitive rules.
arXiv Detail & Related papers (2021-11-16T07:45:41Z) - Proceedings of the 9th International Symposium on Symbolic Computation
in Software Science [0.0]
This volume contains papers presented at the Ninth International Symposium on Symbolic Computation in Software Science, SCSS 2021.
The purpose of SCSS is to promote research on theoretical and practical aspects of symbolic computation in software science, combined with modern artificial intelligence techniques.
arXiv Detail & Related papers (2021-09-06T14:22:11Z) - Discrete Math with Programming: A Principled Approach [0.0]
It has long been argued that discrete math is better taught with programming.
This paper introduces an approach that supports all central concepts of discrete math.
Math and logical statements can be expressed precisely at a high level and be executed on a computer.
arXiv Detail & Related papers (2020-11-28T03:41:27Z) - Machine Number Sense: A Dataset of Visual Arithmetic Problems for
Abstract and Relational Reasoning [95.18337034090648]
We propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG)
These visual arithmetic problems are in the form of geometric figures.
We benchmark the MNS dataset using four predominant neural network models as baselines in this visual reasoning task.
arXiv Detail & Related papers (2020-04-25T17:14:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.