Open Access

ARTICLE

Binary Oriented Feature Selection for Valid Product Derivation in Software Product Line

Muhammad Fezan Afzal¹, Imran Khan¹, Javed Rashid^1,2,3, Mubbashar Saddique^4,*, Heba G. Mohamed⁵

1 Department of CS&SE, International Islamic University, Islamabad, 44000, Pakistan
2 Information Technology Services, University of Okara, Okara, 56300, Pakistan
3 Departmet of Computer Science, MLC Lab, Okara, 56300, Pakistan
4 Department of Computer Science & Engineering, University of Engineering & Technology Lahore, Narowal Campus, Narowal, 51601, Pakistan
5 Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Mubbashar Saddique. Email:

(This article belongs to the Special Issue: The Next Generation of Artificial Intelligence and the Intelligent Internet of Things)

Computers, Materials & Continua 2023, 76(3), 3653-3670. https://doi.org/10.32604/cmc.2023.041627

Received 29 April 2023; Accepted 04 July 2023; Issue published 08 October 2023

Abstract

Software Product Line (SPL) is a group of software-intensive systems that share common and variable resources for developing a particular system. The feature model is a tree-type structure used to manage SPL’s common and variable features with their different relations and problem of Crosstree Constraints (CTC). CTC problems exist in groups of common and variable features among the sub-tree of feature models more diverse in Internet of Things (IoT) devices because different Internet devices and protocols are communicated. Therefore, managing the CTC problem to achieve valid product configuration in IoT-based SPL is more complex, time-consuming, and hard. However, the CTC problem needs to be considered in previously proposed approaches such as Commonality Variability Modeling of Features (COVAMOF) and Genarch + tool; therefore, invalid products are generated. This research has proposed a novel approach Binary Oriented Feature Selection Crosstree Constraints (BOFS-CTC), to find all possible valid products by selecting the features according to cardinality constraints and cross-tree constraint problems in the feature model of SPL. BOFS-CTC removes the invalid products at the early stage of feature selection for the product configuration. Furthermore, this research developed the BOFS-CTC algorithm and applied it to, IoT-based feature models. The findings of this research are that no relationship constraints and CTC violations occur and drive the valid feature product configurations for the application development by removing the invalid product configurations. The accuracy of BOFS-CTC is measured by the integration sampling technique, where different valid product configurations are compared with the product configurations derived by BOFS-CTC and found 100% correct. Using BOFS-CTC eliminates the testing cost and development effort of invalid SPL products.

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Keywords

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