Validating Partial Configurations of Product Lines

Al-Hajjaji, Mustafa; Ryssel, Uwe; Schulze, Michael

doi:10.1145/3302333.3302337

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…The validation partial configurations approach has been proposed to deliberate different cases such as the selection, attributes, and constraints of features, and this technique was applied with the industry variant tool known as pure::variants. This approach helps to validate the partial configurations for different real-world software product lines [21]. Wang et al presented their work with a focus on test case minimization [22].…”

Section: Related Workmentioning

confidence: 99%

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

et al. 2023

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Currently, software development is more associated with families of configurable software than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking. Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment’s time and financial constraints, these features are rendered unfeasible. Thus, testing subsets of configured products is one approach to solving this issue. To reduce the testing effort and obtain better results, alternative methods for testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately, the CIT method produces unscalable solutions for large SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, should also be considered. In this article, we proposed a search-based software engineering solution using multi-objective evolutionary algorithms (MOEAs). In particular, the research was applied to different types of MOEA method: the Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-dominant Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms were examined in the context of distinct objectives and two quality indicators. The results revealed how the feature model attributes, implementation context, and number of objectives affected the performances of the algorithms.

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Section: Related Workmentioning

confidence: 99%

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

et al. 2023

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show abstract

“…The validation partial configurations approach has been proposed to deliberate different cases like selection, attributes and constraints of features and this technique were applied with the industry variant tool known as pure::variants. This approach helps to validate the partial configurations for different real-world software product lines [21]. Wang et al presented his work with a focus on test cases minimization [22].…”

Section: Related Workmentioning

confidence: 99%

Software Product Lines Maintenance using Many Objectives Optimization Techniques

Jamil¹,

Nour²,

Alotaibi³

et al. 2023

Preprint

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Currently, software development is more associated with families of configurable software rather than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking (SPL). Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment's time and financial constraints, these features are rendered unfeasible. Testing subsets of configured products is thus one approach to solving this issue. In order to reduce testing effort and get better results, alternative methods of testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately CIT method produces unscalable solutions for large size SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, have also been considered. In this article, we have proposed a search-based software engineering solution using multi-objective optimization algorithms (MOEAs). In particular, the research is applied to different types of MOEA methods; Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-Dominated Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms are examined in the context of distinct objectives and two quality indicators. The results revealed how feature model attributes, implementation context and the number of objectives affect the performance of the algorithms.

show abstract

Validating Partial Configurations of Product Lines

Cited by 2 publications

References 17 publications

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

Software Product Lines Maintenance using Many Objectives Optimization Techniques

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