DIVERSIFY: Ecology-inspired software evolution for diversity emergence

Baudry, Benoît; Monperrus, Martin; Mony, Cendrine; Chauvel, Franck; Fleurey, Franck; Clarke, Siobhán

doi:10.1109/csmr-wcre.2014.6747203

Cited by 10 publications

(14 citation statements)

References 31 publications

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“…different match set). (5) The mutation score of a test suite is calculated as the ratio of mutants killed by some tests wrt. the total number of mutants.…”

Section: Motivation: Testing Of Dsl Toolsmentioning

confidence: 99%

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Iterative Generation of Diverse Models for Testing Specifications of DSL Tools

Semeráth

Varró

2018

Fundamental Approaches to Software Engineering

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The validation of modeling tools of custom domain-specific languages (DSLs) frequently relies upon an automatically generated set of models as a test suite. While many software testing approaches recommend that this test suite should be diverse, model diversity has not been studied systematically for graph models. In the paper, we propose diversity metrics for models by exploiting neighborhood shapes as abstraction. Furthermore, we propose an iterative model generation technique to synthesize a diverse set of models where each model is taken from a different equivalence class as defined by neighborhood shapes. We evaluate our diversity metrics in the context of mutation testing for an industrial DSL and compare our model generation technique with the popular model generator Alloy.

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“…different match set). (5) The mutation score of a test suite is calculated as the ratio of mutants killed by some tests wrt. the total number of mutants.…”

Section: Motivation: Testing Of Dsl Toolsmentioning

confidence: 99%

“…While software diversity is widely studied [5], existing diversity metrics for graph models are much less elaborate [43]. Model comparison techniques [38] frequently rely upon the existence of node identifiers, which can easily lead to many isomorphic models.…”

Section: Introductionmentioning

confidence: 99%

Iterative Generation of Diverse Models for Testing Specifications of DSL Tools

Semeráth

Varró

2018

Fundamental Approaches to Software Engineering

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“…The EU funded, multi-disciplinary project DEVERISTY [1] is possibly the most related work to our research. DEVERISTY exploits the analogy with natural ecosystems in order to come to better principles and mechanisms for handling the emergence of diversity in collaborative adaptive systems.…”

Section: Related Workmentioning

confidence: 99%

Bridging Ecology and Cloud: Transposing Ecological Perspective to Enable Better Cloud Autoscaling

Chen

Bahsoon

2017

Software Architecture for Big Data and the Cloud

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“…Software diversity [3] is an indicator of potential cascading failure to quantify cloud fault tolerance capabilities. Table I presents our set of operators O including 7 mutation operators and 1 crossover (SwitchOperator) operator.…”

Section: Validationmentioning

confidence: 99%

“…Internally, Sputnik maintains an elitist group of mutation operators that are relevant to improve a fitness function score. To enhance operators selection, Sputnik, considers the current fitness scores reached by a solution, and selects the most relevant mutator in elite groups to improve next generation 3 . As illustrated in Figure 1, Sputnik takes as inputs an initial population and a generation number.…”

Section: Sputnik: An Hyper-heuristic For An Efficient Self-adaptmentioning

confidence: 99%

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

Kateb

Fouquet

Bourcier

et al. 2014

2014 14th International Conference on Quality Software

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Abstract-Elasticity is a key feature for cloud infrastructures to continuously align allocated computational resources to evolving hosted software needs. This is often achieved by relaxing quality criteria, for instance security or privacy because quality criteria are often conflicting with performance. As an example, software replication could improve scalability and uptime while decreasing privacy by creating more potential leakage points. The conciliation of these conflicting objectives has to be achieved by exhibiting trade-offs. Multi-Objective Evolutionary Algorithms (MOEAs) have shown to be suitable candidates to find these trade-offs and have been even applied for cloud architecture optimizations. Still though, their runtime efficiency limits the widespread adoption of such algorithms in cloud engines, and thus the consideration of quality criteria in clouds. Indeed MOEAs produce many dead-born solutions because of the Darwinian inspired natural selection, which results in a resources wastage. To tackle MOEAs efficiency issues, we apply a process similar to modern biology. We choose specific artificial mutations by anticipating the optimization effect on the solutions instead of relying on the randomness of natural selection. This paper introduces the Sputnik algorithm, which leverages the past history of actions to enhance optimization processes such as cloud elasticity engines. We integrate Sputnik in a cloud elasticity engine, dealing with performance and quality criteria, and demonstrate significant performance improvement, meeting the runtime requirements of cloud optimization.

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DIVERSIFY: Ecology-inspired software evolution for diversity emergence

Cited by 10 publications

References 31 publications

Iterative Generation of Diverse Models for Testing Specifications of DSL Tools

Iterative Generation of Diverse Models for Testing Specifications of DSL Tools

Bridging Ecology and Cloud: Transposing Ecological Perspective to Enable Better Cloud Autoscaling

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

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