Visualising the Search Landscape of the Triangle Program

Langdon, William B.; Veerapen, Nadarajen; Ochoa, Gabriela

doi:10.1007/978-3-319-55696-3_7

Cited by 21 publications

(14 citation statements)

References 55 publications

(60 reference statements)

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“…They call this phenomenon positive epistasis [28]. Other work has since confirmed the existence of mutational robustness [16,21] Our RQ1 can be considered as a conceptual replication [35] of the work by Schulte and colleague. Our results mitigate two threats to the validity of Schulte's results: our methodology mitigates internal threats, by using another tool to perform program transformations, and our experiment mitigates external threats by transforming Java programs (instead of C).…”

Section: Plasticity Of Softwaresupporting

confidence: 59%

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: Plasticity Of Softwaresupporting

confidence: 59%

A journey among Java neutral program variants

Harrand

Allier

Rodriguez-Cancio

et al. 2019

Genet Program Evolvable Mach

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“…Traditionally, the space of program mutants has been thought to be relatively disjoint, and with few good programs. However, recent work [6,7,12,17] suggests that many changes do not impact the tness of the mutants. This may either point to the programs being quite robust or to the test suite not providing enough coverage -which is relevant to mutation testing.…”

Section: Introductionmentioning

confidence: 99%

Modelling genetic improvement landscapes with local optima networks

Veerapen

Daolio

Ochoa

2017

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Local optima networks are a compact representation of the global structure of a search space. They can be used for analysis and visualisation. This paper provides one of the rst analyses of program search spaces using local optima networks. These are generated by sampling the search space by recording the progress of an Iterated Local Search algorithm. Source code mutations in comparison and Boolean operators are considered. The search spaces of two small benchmark programs, the triangle and TCAS programs, are analysed and visualised. Results show a high level of neutrality, i.e. connected test-equivalent mutants. It is also generally relatively easy to nd a path from a random mutant to a mutant that passes all test cases. CCS CONCEPTS•Software and its engineering → Search-based software engineering; •Computing methodologies → Randomized search;

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“…The main focus of search space analysis has been on spaces for automated program repair. For example, the exhaustive first-order mutation search space exploration for the triangle program published only in 2017 [22]. In all such studies only the pass or failure of a test case on compilable program variants is considered for the purpose of fitness evaluation, in contrast to work on non-functional property improvement.…”

Section: Search Space Analysis For Functional Improvementmentioning

confidence: 99%

“…-remains open. Papers arguing for a more informed choice of various elements of the GI approach are scarce [7,21,22] and largely concerned with stating the need for further research in this direction [13,20,28,33,43,44]. Almost all GI work to date, for instance, employs Genetic Programming as its key search technique [30].…”

mentioning

confidence: 99%

A survey of genetic improvement search spaces

Petke

Alexander

Barr

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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Genetic Improvement (GI) uses automated search to improve existing software. Most GI work has focused on empirical studies that successfully apply GI to improve software's running time, fix bugs, add new features, etc. There has been little research into why GI has been so successful. For example, genetic programming has been the most commonly applied search algorithm in GI. Is genetic programming the best choice for GI? Initial attempts to answer this question have explored GI's mutation search space. This paper summarises the work published on this question to date. CCS CONCEPTS • Software and its engineering → Search-based software engineering;

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Visualising the Search Landscape of the Triangle Program

Cited by 21 publications

References 55 publications

A journey among Java neutral program variants

A journey among Java neutral program variants

Modelling genetic improvement landscapes with local optima networks

A survey of genetic improvement search spaces

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