locoGP

Cody-Kenny, Brendan; Galván, Edgar; Barrett, Stephen

doi:10.1145/2739482.2768419

Cited by 20 publications

(2 citation statements)

References 39 publications

(57 reference statements)

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“…We also require the programs to be relatively small, so we could run thousands of experiments in reasonable time and avoid the, often very costly, profiling stage of the GI process [6], targeting the whole software instead. With those restrictions in mind, we chose to use the set of 9 sort algorithms and the triangle example provided with the first release of Gin [23] in our study, for which improvements have previously been found using GI [9].…”

Section: Dataset and Toolsmentioning

confidence: 99%

Impact of Test Suite Coverage on Overfitting in Genetic Improvement of Software

Lim

Guizzo

Petke

2020

Search-Based Software Engineering

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Genetic Improvement (GI) uses automated search to improve existing software. It can be used to improve runtime, energy consumption, fix bugs, and any other software property, provided that such property can be encoded into a fitness function. GI usually relies on testing to check whether the changes disrupt the intended functionality of the software, which makes test suites important artefacts for the overall success of GI. The objective of this work is to establish which characteristics of the test suites correlate with the effectiveness of GI. We hypothesise that different test suite properties may have different levels of correlation to the ratio between overfitting and non-overfitting patches generated by the GI algorithm. In order to test our hypothesis, we perform a set of experiments with automatically generated test suites using EvoSuite and 4 popular coverage criteria. We used these test suites as input to a GI process and collected the patches generated throughout such a process. We find that while test suite coverage has an impact on the ability of GI to produce correct patches, with branch coverage leading to least overfitting, the overfitting rate was still significant. We also compared automatically generated tests with manual, developer-written ones and found that while manual tests had lower coverage, the GI runs with manual tests led to less overfitting than in the case of automatically generated tests. Finally, we did not observe enough statistically significant correlations between the coverage metrics and overfitting ratios of patches, i.e., the coverage of test suites cannot be used as a linear predictor for the level of overfitting of the generated patches.

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Section: Dataset and Toolsmentioning

confidence: 99%

Impact of Test Suite Coverage on Overfitting in Genetic Improvement of Software

Lim

Guizzo

Petke

2020

Search-Based Software Engineering

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“…Depending on the particular variant of their framework, line-level or expression-level changes are possible. The locoGP [10] framework developed by Cody-Kenny et al evolves entire Java AST's and acts as an of-the-shelf optimisation tool, while allowing limited customisation via its itness function.…”

Section: Related Workmentioning

confidence: 99%

Gin

Brownlee

Petke

Alexander

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Genetic improvement (GI) is a young ield of research on the cusp of transforming software development. GI uses search to improve existing software. Researchers have already shown that GI can improve human-written code, ranging from program repair to optimising run-time, from reducing energy-consumption to the transplantation of new functionality. Much remains to be done. The cost of re-implementing GI to investigate new approaches is hindering progress. Therefore, we present Gin, an extensible and modiiable toolbox for GI experimentation, with a novel combination of features. Instantiated in Java and targeting the Java ecosystem, Gin automatically transforms, builds, and tests Java projects. Out of the box, Gin supports automated test-generation and source code proiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI. CCS CONCEPTS• Software and its engineering → Software notations and tools; Search-based software engineering;

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Marketing Meets Data Science: Bridging the Gap

Moscato

Vries

2019

Business and Consumer Analytics: New Ideas

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locoGP

Cited by 20 publications

References 39 publications

Impact of Test Suite Coverage on Overfitting in Genetic Improvement of Software

Impact of Test Suite Coverage on Overfitting in Genetic Improvement of Software

Gin

Marketing Meets Data Science: Bridging the Gap

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