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

Zuo, Shengjie; Blot, Aymeric; Petke, Justyna

doi:10.1145/3520304.3534004

Cited by 5 publications

(3 citation statements)

References 71 publications

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“…GI tool Recent survey of GI tooling, revealed that [13] few GI tools can be easily applied to unseen software. After closer investigation we chose Gin [4], as it is the only one to implement fitness functions for at least two non-functional software properties, namely runtime and memory consumption.…”

Section: Methodsmentioning

confidence: 99%

“…In fact finding more memory efficient versions of software can be beneficial to it's speed by saving expensive garbage collection and page swapping operations. Although multiobjective search algorithms seem best fit for this problem domain, to the best of our knowledge, there is no tool available that provides this facility, despite such work being proposed in the past [13].…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective Genetic Improvement: A Case Study with EvoSuite

Callan

Petke²

2022

Lecture Notes in Computer Science

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Automated multi-objective software optimisation offers an attractive solution to software developers wanting to balance often conflicting objectives, such as memory consumption and execution time. Work on using multi-objective search-based approaches to optimise for such non-functional software behaviour has so far been scarce, with tooling unavailable for use. To fill this gap we extended an existing generalist, open source, genetic improvement tool, Gin, with a multi-objective search strategy, NSGA-II. We ran our implementation on a mature, large software to show its use. In particular, we chose EvoSuite -a tool for automatic test case generation for Java. We use our multi-objective extension of Gin to improve both the execution time and memory usage of EvoSuite. We find improvements to execution time of up to 77.8% and improvements to memory of up to 9.2% on our test set. We also release our code, providing the first open source multi-objective genetic improvement tooling for improvement of memory and runtime for Java.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-objective Genetic Improvement: A Case Study with EvoSuite

Callan

Petke²

2022

Lecture Notes in Computer Science

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“…Similarly Gabin An [1] proposed PyGGI for Python. Nevertheless recently a user study said that GI lacked user friendly tools [12]. To address this Magpie [2] was released last year as an open source project.…”

Section: Genetic Improvement With Magpiementioning

confidence: 99%

Genetic Improvement of OLC and H3 with Magpie

Langdon,

Alexander

2023

2023 IEEE/ACM International Workshop on Genetic Improvement (GI)

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Using MAGPIE (Machine Automated General Performance Improvement via Evolution of software) we show genetic improvement GI can reduce the cache load of existing computer programs. Cache miss reduction is tested on two industrial open source C programs (Google's Open Location Code OLC and Uber's Hexagonal Hierarchical Spatial Index H3) and two C++ 2D photograph image processing tasks, counting pixels and OpenCV's SEEDS segmentation algorithm. Magpie's patches functionally generalise. In one case they reduce data misses on the highest performance L1 cache by 47%. CCS CONCEPTS• Software and its engineering → Search-based software engineering.

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Multi-objective improvement of Android applications

Callan,

Petke

2024

Autom Softw Eng

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Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. We propose a practical approach and the first open-source tool, GIDroid for multi-objective automated improvement of Android apps. In particular, we use Genetic Improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic Improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64% of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35%, and memory use by up to 33% in these apps.

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

Cited by 5 publications

References 71 publications

Multi-objective Genetic Improvement: A Case Study with EvoSuite

Multi-objective Genetic Improvement: A Case Study with EvoSuite

Genetic Improvement of OLC and H3 with Magpie

Multi-objective improvement of Android applications

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