Uniform Edit Selection for Genetic Improvement: Empirical Analysis of Mutation Operator Efficacy

Smigielska, M; Blot, Aymeric; Petke, Justyna

doi:10.1109/gi52543.2021.00009

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…The use of empirical analysis in GI attempts to quantify an overall fitness landscape in order to derive the most appropriate mutations or parameter settings to reach high-utility areas [15]. This is related to our approach of using historical information of the fitness landscape, relative to the modifications that were made in each generation, to inform future decisions on how the GI system proceeds.…”

Section: Related Workmentioning

confidence: 99%

Using phylogenetic analysis to enhance genetic improvement

Rainford

Porter

2022

Proceedings of the Genetic and Evolutionary Computation Conference

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Genetic code improvement systems start from an existing piece of program code and search for alternative versions with better performance according to a metric of interest. The search space of source code is a large, rough fitness landscape which can be extremely difficult to navigate. Most approaches to enhancing search capability in this domain involve either novelty search, where low-fitness areas of the search space are remembered and avoided, or formal analysis which attempts to find high-utility parameterizations for the genetic improvement (GI) process. In this paper we propose the use of phylogenetic analysis over genetic history to understand how different mutations and crossovers affect the fitness of a population over time for a particular problem; we then use the results of that analysis to tune a GI process during its operation to enhance its ability to locate better program candidates. Using phylogenetic analysis on 600 runs of a genetic improver targeting a hash function, we demonstrate how the results of this analysis yield tuned mutation types over the course of a GI process (dynamically and continually set according to individual's ancestors' ranks within the population) to give hash functions with over 20% improved fitness compared to a baseline GI process.

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Section: Related Workmentioning

confidence: 99%

Using phylogenetic analysis to enhance genetic improvement

Rainford

Porter

2022

Proceedings of the Genetic and Evolutionary Computation Conference

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“…Existing mutations can also be removed, as a way to decrease bloat, keep the overall sequence length reasonable, and avoid overfitting. The different types of edits are usually considered with fixed probabilities, proportions of which can significantly impact search performance [76]. Genetic programming (GP) [36,54], on the other hand, combines both mutations and crossover to evolve populations of software variants.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of genetic improvement tools for improvement of non-functional properties of software

Zuo

Blot

Petke

2022

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Genetic improvement (GI) improves both functional properties of software, such as bug repair, and non-functional properties, such as execution time, energy consumption, or source code size. There are studies summarising and comparing GI tools for improving functional properties of software; however there is no such study for improvement of its non-functional properties using GI. Therefore, this research aims to survey and report on the existing GI tools for improvement of non-functional properties of software. We conducted a literature review of available GI tools, and ran multiple experiments on the found open-source tools to examine their usability. We applied a cross-testing strategy to check whether the available tools can work on different programs.Overall, we found 63 GI papers that use a GI tool to improve nonfunctional properties of software, within which 31 are accompanied with open-source code. We were able to successfully run eight GI tools, and found that ultimately only two -Gin and PyGGI-can be readily applied to new general software. CCS CONCEPTS• Software and its engineering → Search-based software engineering; Software evolution; Extra-functional properties; • General and reference → Surveys and overviews.

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Deep imperative mutations have less impact

Langdon,

Clark

2024

Autom Softw Eng

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Information theory and entropy loss predict deeper more hierarchical software will be more robust. Suggesting silent errors and equivalent mutations will be more common in deeper code, highly structured code will be hard to test, so explaining best practise preference for unit testing of small methods rather than system wide analysis. Using the genetic improvement (GI) tool MAGPIE, we measure the impact of source code mutations and how this varies with execution depth in two diverse multi-level nested software. gem5 is a million line single threaded state-of-the-art C++ discrete time VLSI circuit simulator, whilst PARSEC VIPS is a non-deterministic parallel computing multi-threaded image processing benchmark written in C. More than 28–53% of mutants compile and generate identical results to the original program. We observe 12% and 16% Failed Disruption Propagation (FDP). Excluding internal errors, exceptions and asserts, here most faults below about 30 nested function levels which are Executed and Infect data or divert control flow are not Propagated to the output, i.e. these deep PIE changes have no visible external effect. Suggesting automatic software engineering on highly structured code will be hard.

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Uniform Edit Selection for Genetic Improvement: Empirical Analysis of Mutation Operator Efficacy

Cited by 7 publications

References 34 publications

Using phylogenetic analysis to enhance genetic improvement

Using phylogenetic analysis to enhance genetic improvement

Evaluation of genetic improvement tools for improvement of non-functional properties of software

Deep imperative mutations have less impact

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