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What can automatic programming learn from theoretical computer science?

Johnson, Colin G. (2002) What can automatic programming learn from theoretical computer science? In: Yao, Xin, ed. Proceedings of the 2002 U.K. Workshop on Computational Intelligence: UKCI-02. University of Birmingham Press. ISBN 0-7044-2368-5. (KAR id:13729)

Abstract

This paper considers two (seemingly) radically different perspectives on the construction of software. On one hand, search-based heuristics such as genetic programming. On the other hand, the theories of programming which underpin mathematical program analysis and formal methods. The main part of the paper surveys possible links between these perspectives. In particular the contrast between inductive and deductive approaches to software construction are studied, and various suggestions are made as to how randomized search heuristics can be combined with formal approaches to software construction without compromising the rigorous provability of the results. The aim of the ideas proposed is to improve the efficiency, effectiveness and safety of search-based automatic programming.

Item Type: Book section
Uncontrolled keywords: genetic programming, software engineering, search, static analysis, model checking, specification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Birmingham (https://ror.org/03angcq70)
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:59 UTC
Last Modified: 12 Jul 2022 10:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13729 (The current URI for this page, for reference purposes)

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