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2024
Conference article
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Preserving non-functional requirements in goal models using meta-models of the software product lines
Jadoon G.
Non-functional requirements (NFRs) play a critical role in software product line (SPL) engineering, ensuring products meet essential criteria beyond mere functionality. However, preserving NFRs across product variants induces considerable challenges, particularly in goal-oriented SPLE where goals guide product derivation. This research proposes a novel framework to preserve NFRs in goal models using meta-models of SPLs and manage inconsistent NFRs. The framework utilizes product and domain meta-models to accurately capture and represent NFRs, addressing construct validity concerns. This research aims to enhance the credibility and generalizability of findings in SPL engineering, contributing to the advancement of goal-oriented modeling and NFR preservation practices.DOI: 10.1145/3646548.3676541
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Jadoon G.
Non-functional requirements (NFRs) play a critical role in software product line (SPL) engineering, ensuring products meet essential criteria beyond mere functionality. However, preserving NFRs across product variants induces considerable challenges, particularly in goal-oriented SPLE where goals guide product derivation. This research proposes a novel framework to preserve NFRs in goal models using meta-models of SPLs and manage inconsistent NFRs. The framework utilizes product and domain meta-models to accurately capture and represent NFRs, addressing construct validity concerns. This research aims to enhance the credibility and generalizability of findings in SPL engineering, contributing to the advancement of goal-oriented modeling and NFR preservation practices.DOI: 10.1145/3646548.3676541
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2024
Journal article
Open Access
Dynamic property preservation in AIoT: a machine learning approach for data-efficient model transformation
Jadoon G., Ahmed A., Ud Din I., Almogren A., Zareei M., Biswal R. R., Altameem A.
Model-driven development (MDD) in the Artificial Intelligence of Things (AIoT) domain faces significant challenges in ensuring the consistency and preservation of model properties during transformations, often leading to system inconsistencies. This research introduces the Property Preservation Framework (PPF), a novel approach fortified with a Markov chain methodology, specifically designed to address these challenges. The PPF integrates formal procedures and constraint-checking methods to systematically validate and preserve model properties, thereby enhancing the reliability of AIoT systems. Through empirical evaluations, the framework has demonstrated its ability to efficiently determine and verify model characteristics at various transformation stages, significantly reducing the incidence of property violations. The results indicate that the PPF not only ensures overall consistency and reliability but also optimizes resource allocation, thereby enhancing data efficiency during the property validation and preservation processes. These advancements make a substantial contribution to the domain of MDD, providing developers with the methodology to execute model transformations that accurately reflect system requirements and behaviors in AIoT ecosystems. The findings underscore the potential of the PPF to revolutionize AIoT development by ensuring high-quality, dependable, and efficient modeling outcomes.Source: IEEE ACCESS, vol. 12, pp. 130707-130722
DOI: 10.1109/access.2024.3454717
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Jadoon G., Ahmed A., Ud Din I., Almogren A., Zareei M., Biswal R. R., Altameem A.
Model-driven development (MDD) in the Artificial Intelligence of Things (AIoT) domain faces significant challenges in ensuring the consistency and preservation of model properties during transformations, often leading to system inconsistencies. This research introduces the Property Preservation Framework (PPF), a novel approach fortified with a Markov chain methodology, specifically designed to address these challenges. The PPF integrates formal procedures and constraint-checking methods to systematically validate and preserve model properties, thereby enhancing the reliability of AIoT systems. Through empirical evaluations, the framework has demonstrated its ability to efficiently determine and verify model characteristics at various transformation stages, significantly reducing the incidence of property violations. The results indicate that the PPF not only ensures overall consistency and reliability but also optimizes resource allocation, thereby enhancing data efficiency during the property validation and preservation processes. These advancements make a substantial contribution to the domain of MDD, providing developers with the methodology to execute model transformations that accurately reflect system requirements and behaviors in AIoT ecosystems. The findings underscore the potential of the PPF to revolutionize AIoT development by ensuring high-quality, dependable, and efficient modeling outcomes.Source: IEEE ACCESS, vol. 12, pp. 130707-130722
DOI: 10.1109/access.2024.3454717
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See at:
IEEE Access 
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