Software Product Line Test List Generation based on Harmony Search Algorithm with Constraints Support

Alsewari, AbdulRahman A.; Kabir, Muhammad Nomani; Zamli, Kamal Z.; Alaofi, Khalid S.

doi:10.14569/ijacsa.2019.0100176

Cited by 6 publications

(4 citation statements)

References 7 publications

(7 reference statements)

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“…This approach requires the minimum number of test cases for all the features that depend on the required interaction degree. The obtained results show that the SPL-HS technique can challenge existing SPL testing approaches to generate optimal results [20]. 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.…”

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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“…Population-based intelligence algorithms are widely known. Some recognized swarm intelligence optimization algorithms inspired by nature are mainly include: genetic al-gorithm (GA) [4], particle swarm optimization algorithm (PSO) [5], Moth-flame optimization algorithm (MFO) [6], ant colony optimization algorithm (ACO) [7], harmony search algorithm (HS) [8], multi-verse optimization algorithm (MVO) [3], Harris hawks optimization algorithm (HHO) [9], squirrel search algorithm (SSO) [10], and elephant herding optimization algorithm (EHO) [11]. These algorithms are inspired by different natural phenomena.…”

Section: Related Workmentioning

confidence: 99%

“…Further, in the actual scenario, with the development of teaching activities, the teaching level of teachers should be gradually improved rather than randomly assigned. Therefore, a linear increasing 𝑇 𝐹 is designed to model its adaptive changes with iterations in this subsection, as shown in Equation (7).…”

Section: A Linear Increasing Teaching Factormentioning

confidence: 99%

Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

Chen

Zhang

et al. 2023

Evol. Intel.

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The teaching-learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching-learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO's exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

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“…Overall the algorithm performance will improve if a balance between exploration and exploitation features is achieved [1]. Geem et al [13], created the Harmony Search algorithm (HS) by mimicking the creation of new music tune, and it has been used to solve different types of problems by many researchers in different areas such as engineering [16], computer science [17] and many other fields [18].…”

Section: Introductionmentioning

confidence: 99%

Enhancing three variants of harmony search algorithm for continuous optimization problems

Alomoush

Zamli

Alrosan

et al. 2021

IJECE

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Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.

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Software Product Line Test List Generation based on Harmony Search Algorithm with Constraints Support

Cited by 6 publications

References 7 publications

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

Software Product Line Maintenance Using Multi-Objective Optimization Techniques

Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

Enhancing three variants of harmony search algorithm for continuous optimization problems

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