Genetic Improvement of Software: A Comprehensive Survey

Petke, Justyna; Haraldsson, Sæmundur Ó.; Harman, Mark; Langdon, William B.; White, David R.; Woodward, John R.

doi:10.1109/tevc.2017.2693219

Cited by 172 publications

(127 citation statements)

References 181 publications

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“…Here we define the key concepts that we leverage to explore the different regions of Java programs that are prone to the synthesis of neutral program variants. Given an initial program, which comes along with a test suite, we consider three generic program transformations on source code that have been defined in previous work [4,27,33]. These transformations operate on the abstract syntax tree (AST).…”

Section: Background and Definitionsmentioning

confidence: 99%

A journey among Java neutral program variants

Harrand

Allier

Rodriguez-Cancio

et al. 2019

Genet Program Evolvable Mach

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Neutral program variants are alternative implementations of a program, yet equivalent with respect to the test suite. Techniques such as approximate computing or genetic improvement share the intuition that potential for enhancements lies in these acceptable behavioral differences (e.g., enhanced performance or reliability). Yet, the automatic synthesis of neutral program variants, through program transformations remains a key challenge.This work aims at characterizing plastic code regions in Java programs, i.e., the code regions that are modifiable while maintaining functional correctness, according to a test suite. Our empirical study relies on automatic variations of 6 real-world Java programs. First, we transform these programs with three stateof-the-art program transformations: add, replace and delete statements. WeWe would like to thank Amine Benelallam and Paul Bettega for their feedback on this work.

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Section: Background and Definitionsmentioning

confidence: 99%

A journey among Java neutral program variants

Harrand

Allier

Rodriguez-Cancio

et al. 2019

Genet Program Evolvable Mach

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“…The genetic approach to code modification is one of the most popular approaches to automated software improvement [4] with recent progress in automated bug-fixing [5][6][7], optimising non-functional properties [8][9][10], and automatic test-case generation [6].…”

Section: Related Workmentioning

confidence: 99%

Experiments in genetic divergence for emergent systems

McGowan

Wild

Porter

2018

Proceedings of the 4th International Workshop on Genetic Improvement Workshop

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Emergent software systems take a step towards tackling the everincreasing complexity of modern software, by having systems selfassemble from a library of building blocks, and then continually re-assemble themselves from alternative building blocks to learn which compositions of behaviour work best in each deployment environment. One of the key challenges in emergent systems is populating the library of building blocks, and particularly a set of alternative implementations of particular building blocks, which form the runtime search space of optimal behaviour. We present initial work in using a fusion of genetic improvement and genetic synthesis to automatically populate a divergent set of implementations of the same functionality, allowing emergent systems to explore new behavioural alternatives without human input. Our early results indicate this approach is able to successfully yield useful divergent implementations of building blocks which are more suited than any existing alternative for particular operating conditions.

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“…The model is periodically updated with new information and improved with Genetic Improvement (GI) of software [29] procedure. GI is an emerging field from Search Based Software Engineering [30] which uses computational search to improve existing software.…”

Section: Background and Related Workmentioning

confidence: 99%

The use of predictive models in dynamic treatment planning

Haraldsson

Brynjolfsdottir

Woodward

et al. 2017

2017 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-With the expanding load on healthcare and consequent strain on budget, the demand for tools to increase efficiency in treatments is rising. The use of prediction models throughout the treatment to identify risk factors might be a solution. In this paper we present a novel implementation of a prediction tool and the first use of a dynamic predictor in vocational rehabilitation practice. The tool is periodically updated and improved with Genetic Improvement of software. The predictor has been in use for 10 months and is evaluated on predictions made during that time by comparing them with actual treatment outcome. The results show that the predictions have been consistently accurate throughout the patients' treatment. After approximately 3 week learning phase, the predictor classified patients with 100% accuracy and precision on previously unseen data. The predictor is currently being successfully used in a complex live system where specialists have used it to make informed decisions.

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Genetic Improvement of Software: A Comprehensive Survey

Cited by 172 publications

References 181 publications

A journey among Java neutral program variants

A journey among Java neutral program variants

Experiments in genetic divergence for emergent systems

The use of predictive models in dynamic treatment planning

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