Comparison of exact and approximate multi-objective optimization for software product lines

Olaechea, Rafael; Rayside, Derek; Guo, Jianmei; Czarnecki, Krzysztof

doi:10.1145/2648511.2648521

Cited by 68 publications

(40 citation statements)

References 38 publications

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“…For example, a user constraint may be "the system consumes as little memory as possible". To support these cases, configuration approaches based on constraint hierarchies [23] and multi-objective optimization [24] may be combined with our approach. This is one of the future directions that could be explored to enhance this work.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Range Fixes: Interactive Error Resolution for Software Configuration

Xiong

Zhang

Hubaux

et al. 2015

IIEEE Trans. Software Eng.

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To prevent ill-formed configurations, highly configurable software often allows defining constraints over the available options. As these constraints can be complex, fixing a configuration that violates one or more constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix or a very long fix list, difficult for the user to identify the desirable fix; and (2) they do not fully support non-Boolean constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a violated constraint. We have evaluated our approach with three different strategies for handling constraint interactions, on data from nine open source projects over two configuration platforms. The evaluation shows that our notion of range fix leads to mostly simple yet complete sets of fixes, and our algorithm is able to generate fixes within one second for configuration systems with a few thousands options and constraints.

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Section: Limitations and Future Workmentioning

confidence: 99%

Range Fixes: Interactive Error Resolution for Software Configuration

Xiong

Zhang

Hubaux

et al. 2015

IIEEE Trans. Software Eng.

Self Cite

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“…They show that this selection considers different objectives. For example, adaptation and evolution, customization according to user preferences, and so on (Henard et al 2015;Olaechea et al 2014;Sayyad et al 2013).…”

Section: Related Workmentioning

confidence: 99%

“…The works of Sayyad et al (2013a,b) evaluate different multiobjective evolutionary algorithms for SPL configuration, considering different factors to select the products: number of violated rules in the FM, cost, number of used features, and number of faults revealed during the testing activity. Multi-objective selection approaches and exact ones are compared in the work of (Olaechea et al 2014).…”

Section: Related Workmentioning

confidence: 99%

A multi-objective test data generation approach for mutation testing of feature models

Filho

Vergílio

2016

J Softw Eng Res Dev

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Background: Mutation approaches have been recently applied for feature testing of Software Product Lines (SPLs). The idea is to select products, associated to mutation operators that describe possible faults in the Feature Model (FM). In this way, the operators and mutation score can be used to evaluate and generate a test set, that is a set of SPL products to be tested. However, the generation of test sets to kill all the mutants with a reduced, possible minimum, number of products is a complex task. Methods: To help in this task, in a previous work, we introduced a multi-objective approach that includes a representation to the problem, search operators, and two objectives related to the number of test cases and dead mutants. The proposed approach was implemented and evaluated with three representative multi-objective and evolutionary algorithms: NSGA-II, SPEA2 and IBEA, and obtained promising results. Now in the present paper we extend such an approach to include a third objective: the pairwise coverage. The goal 4 is to reveal other kind of faults not revealed by mutation testing and to improve the efficacy of the generated test sets. Results: Results of new studies are reported, showing that both criteria can be satisfied with a reduced number of products. The approach produces diverse good solutions and different sets of impacting factors can be considered. Conclusions: At the end, the tester can either prioritize one objective, by choosing solutions in the extreme points of the fronts or choose solutions with smaller ED values, according to the testing goals and resources.

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“…On the other hand, metaheuristics usually suffer from parameter sensitivity [18]. They often demand a considerable time to tune parameters for finding reasonably approximate solutions [32]. These problems motivated us to explore the feasibility of exact MOCO methods and improve their performance as far as possible.…”

Section: Related Workmentioning

confidence: 99%

Scaling exact multi-objective combinatorial optimization by parallelization

Guo

Zulkoski

Olaechea

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Multi-Objective Combinatorial Optimization (MOCO) is fundamental to the development and optimization of software systems. We propose five novel parallel algorithms for solving MOCO problems exactly and efficiently. Our algorithms rely on off-the-shelf solvers to search for exact Pareto-optimal solutions, and they parallelize the search via collaborative communication, divide-and-conquer, or both. We demonstrate the feasibility and performance of our algorithms by experiments on three case studies of software-system designs. A key finding is that one algorithm, which we call FS-GIA, achieves substantial (even super-linear) speedups that scale well up to 64 cores. Furthermore, we analyze the performance bottlenecks and opportunities of our parallel algorithms, which facilitates further research on exact, parallel MOCO.

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Comparison of exact and approximate multi-objective optimization for software product lines

Cited by 68 publications

References 38 publications

Range Fixes: Interactive Error Resolution for Software Configuration

Range Fixes: Interactive Error Resolution for Software Configuration

A multi-objective test data generation approach for mutation testing of feature models

Scaling exact multi-objective combinatorial optimization by parallelization

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