Genetic improvement in code interpreters and compilers

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

3 Citations (Scopus)

Abstract

Modern compilers provide code optimizations before and during run-time, thus moving required domain knowledge about the compilation process away from the developer and speeding up resulting software. These optimizations are often based on formal proof, or alternatively have recovery paths as backup. Genetic improvement (GI), a field of science utilizing genetic programming, a stochastic optimization technique, has been previously utilized to fix bugs in software and improve non-functional software requirements. This work proposes to research the applicability of GI in an offline phase directly at the interpreter or compiler level. The primary goal is to reformulate existing source code in such a way that existing optimizations can be applied in order to increase performance even further and requiring even less domain knowledge from the developer about a programming language and/or compiler. From these reformulations, patterns can be identified that allow code restructuring without the overhead GI poses.

Original languageEnglish
Title of host publicationSPLASH Companion 2017 - Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications
Subtitle of host publicationSoftware for Humanity
EditorsGail C. Murphy
PublisherACM Press
Pages7-9
Number of pages3
ISBN (Electronic)9781450355148
ISBN (Print)978-1-4503-5514-8
DOIs
Publication statusPublished - 22 Oct 2017
EventSPLASH / OOPSLA 2017 - Vancouver, Canada
Duration: 22 Oct 201727 Oct 2017
http://2017.splashcon.org/home

Publication series

NameSPLASH Companion 2017 - Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity

Conference

ConferenceSPLASH / OOPSLA 2017
CountryCanada
CityVancouver
Period22.10.201727.10.2017
Internet address

Keywords

  • Code Optimization
  • Compilation
  • Genetic Improvement
  • Genetic Programming
  • Non-Functional

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