Software Improvement with Gin: A Case Study

Petke, Justyna; Brownlee, Alexander E. I.

doi:10.1007/978-3-030-27455-9_14

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…(1) Testing Gin with SAT4J, which is the software improved by PyGGI 2.0 in previous work [12]. (2) Testing PyGGI 2.0 with Gson, which is the software improved by Gin in previous work [68].…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…HPROF was included with JDK versions up to 8, and was the original profiling tool integrated within Gin up to v2.0 (White 2017;Brownlee et al 2019). This led to its use in identifying code for improvement in several GI studies (Petke and Brownlee 2019;Petke et al 2023;Brownlee et al 2020), so we include it for comparison within our study. JFR has been bundled with the JDK since version 7, being officially supported since version 9.…”

Section: Profilers and Integration With Ginmentioning

confidence: 99%

Comparing apples and oranges? Investigating the consistency of CPU and memory profiler results across multiple java versions

Watkinson,

Brownlee

2024

Autom Softw Eng

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Profiling is an important tool in the software developer’s box, used to identify hot methods where most computational resources are used, to focus efforts at improving efficiency. Profilers are also important in the context of Genetic improvement (GI) of software. GI applies search-based optimisation to existing software with many examples of success in a variety of contexts. GI generates variants of the original program, testing each for functionality and properties such as run time or memory footprint, and profiling can be used to target the code variations to increase the search efficiency. We report on an experimental study comparing two profilers included with different versions of the Java Development Kit (JDK), HPROF (JDK 8) and Java Flight Recorder (JFR) (JDK 8, 9, and 17), within the GI toolbox Gin on six open-source applications, for both run time and memory use. We find that a core set of methods are labelled hot in most runs, with a long tail appearing rarely. We suggest five repeats enough to overcome this noise. Perhaps unsurprisingly, changing the profiler and JDK dramatically change the hot methods identified, so profiling must be rerun for new JDKs. We also show that using profiling for test case subset selection is unwise, often missing relevant members of the test suite. Similar general patterns are seen for memory profiling as for run time but the identified hot methods are often quite different.

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“…Last year, in Section 3[18], we listed 22 tools, also[11,29,30,31] were published after the GI @ ICSE 2019 workshop.…”

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confidence: 99%

Genetic Improvement @ ICSE 2020

Langdon¹,

Weimer

Petke

et al. 2020

SIGSOFT Softw. Eng. Notes

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Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceed- ings) there was a wide ranging discussion at the eighth inter- national Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the International Conference on Software En- gineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, jus- tifyability, exploitability) and GI benchmarks. We also con- trast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face to face interac- tion. Finally we speculate on how the Coronavirus Covid-19 Pandemic will a ect research next year and into the future.

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Software Improvement with Gin: A Case Study

Cited by 7 publications

References 11 publications

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

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

Comparing apples and oranges? Investigating the consistency of CPU and memory profiler results across multiple java versions

Genetic Improvement @ ICSE 2020

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