Improving Software Product Line Challenges In Globally Distributed Environment Using Modern Strategies

Javaid, Javaria; Kiani, Azaz Ahmed; Hafeez, Yaser; Iltaf, Naima; Ahmed, Faheem; Abbas, Ghulam

doi:10.1109/comtech57708.2023.10165120

Cited by 1 publication

(2 citation statements)

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“…SPLE must be used in addition to global software development (GSD) in order to enhance product quality and customer collaboration because useful knowledge management and reusability are disregarded in GSD [3]. The dynamic variability (DV) management prevailing in GSD based agile SPL poses special difficulties [24]. A promising method to manage DV in SPL and ensure security in Scrum and GSD teams is machine learning (ML) based Dynamic Variability Management (DVM).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, we proposed a framework to manage dynamic variability of features and secure development process in GSD with Agile methodologies using ML. Hence, the core contribution of the study is to recognize challenges/barriers of existing SPL techniques especially in the GSD environment using Agile and propose an approach to mitigate these challenges to improve software quality and team collaboration using ML approach [1,24]. Data mining reveals hidden information patterns that aid in the prioritization of requirements.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Iqbal,

Hafeez,

Almashfi

et al. 2024

CMC

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Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, dynamic feature extraction, and enhanced feature selection in distributed settings, contributing to cyber-resilient ecosystems. Our experiments demonstrate the framework's superiority, emphasizing its potential to boost productivity and security in SPLs. As digital threats evolve, this research catalyzes interdisciplinary collaborations, aligning with the special issue's goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics, privacy, and human values.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%