Hyper-Heuristic Based Product Selection for Software Product Line Testing

Ferreira, Thiago Nascimento; Lima, Jackson A. Prado; Strickler, Andrei; Kuk, Josiel Neumann; Vergílio, Silvia Regina; Pozo, Aurora

doi:10.1109/mci.2017.2670461

Cited by 43 publications

(29 citation statements)

References 43 publications

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“…Their approach was evaluated using four feature models with up to 44 features. Ferreira et al [23,66] presented a hyper-heuristic approach for dynamic selection of evolutionary operators to be applied during the execution of a multi-objective evolutionary algorithm. The hyperheuristic tries to determine the best mutation and crossover operators based on their performance.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the problem is to find a test suite (sequence of valid products) that is likely to be a good solution (in terms of selection and prioritisation). It has been observed that a number of properties of good test suites can be captured by objective functions that map a test suite to a value that represents how 'good' this test suite is according to the properties (see, for example [19,23,29,31,32,48,51,58,61,71,72,74]). For example, a fault might be associated with the interaction of a pair of features and so we might want to test as many such interactions as possible (pairwise coverage).…”

Section: Introductionmentioning

confidence: 99%

“…Since a number of objectives/fitness functions have been identified, the problem of finding a good solution has been expressed as a multi-objective optimisation problem [19,23,24,31,47,48,55,58,71,72,74]. Previous work developed test generation approaches based on evolutionary multi-objective algorithms and found these to be effective [19,23,31,48,58,71,72,74]; a recent survey was provided by Herrejon et al [49].…”

Section: Introductionmentioning

confidence: 99%

“…The literature on test generation from feature models has three important limitations that we address in this paper. First, most previous work has looked at multi-objective optimisation problems, in which there are two or three objectives, but only a few have explored the benefits of many-objective optimisation, with four [23,74] and five objectives [72]. In fact, Herrejon et al [49] explicitly mentioned many-objective search in SPL testing as an open challenge.…”

Section: Introductionmentioning

confidence: 99%

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Many-Objective Test Suite Generation for Software Product Lines

Hierons

Liu

et al. 2020

ACM Trans. Softw. Eng. Methodol.

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Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Many-Objective Test Suite Generation for Software Product Lines

Hierons

Liu

et al. 2020

ACM Trans. Softw. Eng. Methodol.

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“…MOEAs-based HHs were developed for a series of benchmarks. Ferreira et al [55] utilized MOHHs to solve software product line testing. They integrated of the HH component, credit assignment, and selection method (i.e., random and upper confidence bound) into MOEAs, that is, NSGA-II, SPEA2, indicator-based evolutionary algorithm (IBEA), and MOEA based on decomposition (MOEA/D).…”

Section: Hyper-heuristic Reviewmentioning

confidence: 99%

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

et al. 2019

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With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.

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Automated analysis of feature models: Quo vadis?

et al. 2018

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Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.

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Hyper-Heuristic Based Product Selection for Software Product Line Testing

Cited by 43 publications

References 43 publications

Many-Objective Test Suite Generation for Software Product Lines

Many-Objective Test Suite Generation for Software Product Lines

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

Automated analysis of feature models: Quo vadis?

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