Multi-Objective Optimum Solutions for IoT-Based Feature Models of Software Product Line

A software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) a...

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Published in:IEEE access Vol. 6; pp. 12228 - 12239
Main Authors: Abbas, Asad, Farah Siddiqui, Isma, Lee, Scott Uk-Jin, Kashif Bashir, Ali, Ejaz, Waleed, Qureshi, Nawab Muhammad Faseeh
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Complexity
Evolutionary algorithms
Evolutionary computation
Feature extraction
feature modeling
Frequency modulation
Genetic algorithms
Internet of Things
Internet of Things (IoT)
Linear programming
multi-objective optimization
Multiple objective analysis
Optimization
Product lines
Software
Software development
Software product line (SPL)
Sorting algorithms
ISSN:2169-3536, 2169-3536
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Abstract A software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) applications. In the literature, the binary pattern for nested cardinality constraints (BPNCC) approach has been proposed to compute all possible combinations of development features for IoT applications without violating any relationship constraints. Relationship constraints are a predefined set of rules for the selection of features from an FM. Due to high probability of relationship constraints violations, obtaining optimum features combinations from large IoT-based FMs are a challenging task. Therefore, in order to obtain optimum solutions, in this paper, we have proposed multi-objective optimum-BPNCC that consists of three independent paths (first, second, and third). Furthermore, we applied heuristics on these paths and found that the first path is infeasible due to space and execution time complexity. The second path reduces the space complexity; however, time complexity increases due to the increasing group of features. Among these paths, the performance of the third path is best as it removes optional features that are not required for optimization. In experiments, we calculated the outcomes of all three paths that show the significant improvement of optimum solution without constraint violation occurrence. We theoretically prove that this paper is better than previously proposed optimization algorithms, such as a non-dominated sorting genetic algorithm and an indicator-based evolutionary algorithm.
AbstractList A software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) applications. In the literature, the binary pattern for nested cardinality constraints (BPNCC) approach has been proposed to compute all possible combinations of development features for IoT applications without violating any relationship constraints. Relationship constraints are a predefined set of rules for the selection of features from an FM. Due to high probability of relationship constraints violations, obtaining optimum features combinations from large IoT-based FMs are a challenging task. Therefore, in order to obtain optimum solutions, in this paper, we have proposed multi-objective optimum-BPNCC that consists of three independent paths (first, second, and third). Furthermore, we applied heuristics on these paths and found that the first path is infeasible due to space and execution time complexity. The second path reduces the space complexity; however, time complexity increases due to the increasing group of features. Among these paths, the performance of the third path is best as it removes optional features that are not required for optimization. In experiments, we calculated the outcomes of all three paths that show the significant improvement of optimum solution without constraint violation occurrence. We theoretically prove that this paper is better than previously proposed optimization algorithms, such as a non-dominated sorting genetic algorithm and an indicator-based evolutionary algorithm.
Author Qureshi, Nawab Muhammad Faseeh
Ejaz, Waleed
Abbas, Asad
Farah Siddiqui, Isma
Kashif Bashir, Ali
Lee, Scott Uk-Jin
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SubjectTerms Complexity
Evolutionary algorithms
Evolutionary computation
Feature extraction
feature modeling
Frequency modulation
Genetic algorithms
Internet of Things
Internet of Things (IoT)
Linear programming
multi-objective optimization
Multiple objective analysis
Optimization
Product lines
Software
Software development
Software product line (SPL)
Sorting algorithms
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Title Multi-Objective Optimum Solutions for IoT-Based Feature Models of Software Product Line
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