Binary multi-objective PSO and GA for adding new features into an existing product line

Mamun, Abdullah Al; Djatmiko, Fahim; Das, Mridul

doi:10.1109/iccitechn.2016.7860263

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…Linsbauer et al (2014) analysed seventeen feature models using genetic programming and compared results with previous results. Mamun et al (2016) analysed next release problem using multi-objective genetic algorithms. The author also modified mathematical model and measure feature objectives for obtaining sub-optimal results.…”

Section: Television Resolution High Definition Low Definition Size Lomentioning

confidence: 99%

Feature selection optimisation of software product line using metaheuristic techniques

Yadav

Kumari

Chhikara

2020

IJES

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The role of software product line (SPL) is very important in representing the same system with multiple variants. Feature models are used to define SPL. In this paper, genetic algorithm (GA), hyper-heuristic algorithm and particle swarm optimisation (PSO) have been applied for feature selection optimisation in SPL. Also, an improved fitness function is applied for optimisation of features in SPL. The objective function is designed by taking reusability and consistency of features (components) into consideration. Furthermore, we have used a case study and discussed about software product line in detail. A non-parametric test, i.e., Kruskal-Wallis test has been performed to analyse performance and computation time of 20 to 1,000 features sets and identify core features. Through extensive experimental analysis, it is observed that PSO outperforms GA and hyper-heuristic algorithm.

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Section: Television Resolution High Definition Low Definition Size Lomentioning

confidence: 99%

Feature selection optimisation of software product line using metaheuristic techniques

Yadav

Kumari

Chhikara

2020

IJES

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“…Qualitative and quantitative features are considered [3]: qualitative features are represented using a qualifier tag in an ordinal scale; meanwhile, quantitative features are represented as numerical values. Features are represented either as integers [4] or binary [5] values. The authors in [4] represented features as a value or a binary string, as stated in Equation 1:…”

Section: Literature Reviewmentioning

confidence: 99%

“…The algorithm takes into account the product/category level objectives that require the total property value for the product or category to be "more than" or "less than" a certain value. Every Property Value (PV) of every feature associated with such constraints will be quantified using Equation (5).…”

Section: Jaguar Selection Algorithmmentioning

confidence: 99%

SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives

et al. 2019

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Software Product Lines (SPLs) can aid modern ecosystems by rapidly developing large-scale software applications. SPLs produce new software products by combining existing components that are considered as features. Selection of features is challenging due to the large number of competing candidate features to choose from, with different properties, contributing towards different objectives. It is also a critical part of SPLs as they have a direct impact on the properties of the product. There have been a number of attempts to automate the selection of features. However, they offer limited flexibility in terms of specifying objectives and quantifying datasets based on these objectives, so they can be used by various selection algorithms. In this research we introduce a novel feature selection approach that supports multiple multi-level user defined objectives. A novel feature quantification method using twenty operators, capable of treating text-based and numeric values and three selection algorithms called Falcon, Jaguar, and Snail are introduced. Falcon and Jaguar are based on greedy algorithm while Snail is a variation of exhaustive search algorithm. With an increase in 4% execution time, Jaguar performed 6% and 8% better than Falcon in terms of added value and the number of features selected.

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Development and Evolution of Software Product Lines Driven by Stakeholder Beliefs

Burgueño,

Horcas,

Kienzle

2023

Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume A

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Binary multi-objective PSO and GA for adding new features into an existing product line

Cited by 3 publications

References 16 publications

Feature selection optimisation of software product line using metaheuristic techniques

Feature selection optimisation of software product line using metaheuristic techniques

SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives

Development and Evolution of Software Product Lines Driven by Stakeholder Beliefs

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