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2025
Journal article
Open Access
Model transformation and property preservation in rigorous software development: a systematic literature review
Jadoon G., Ter Beek M. H., Ferrari A.
Rigorous software development involves using highly structured methods and processes in software and system engineering to ensure that the developed products are correct, reliable, and robust. In this context, model-driven development (MDD) has emerged as a development paradigm that emphasizes designing software systems by means of graphical or textual models at different levels of abstraction, which capture different aspects or dimensions of the system-to-be. At the core of MDD is model transformation, which is the process of translating one model into another, according to specific rules. Property preservation in MDD refers to maintaining specific properties of the system model during transformations, including structural, behavioral, and domain-specific constraints. Over the past decades, research on model transformation and property preservation has seen several contributions. In this paper, we present a systematic literature review (SLR) to compile information on study demographics, model properties considered, techniques to ensure property preservation, and other aspects. In addition, through thematic analysis, we highlight significant challenges and benefits associated with model transformation and property preservation. We analyze 182 research studies published between 2000 and 2024. Most of the studies concern case studies (52) and rigorous analysis (47), while experimental studies using human subjects are limited (1). Formal logic is the most commonly used transformation language, used in 35 studies, while the Unified Modeling Language (UML) is also used for source (55) and target (21) modeling. A total of 93 of the studies performed system testing on models, while 44 of the studies used transformation rules to verify transformation properties. Among the verified model properties, 64 studies focused on consistency management, while 4 are related to model maintainability and reuse. We conclude from our SLR that property preservation could be improved by using model-specific verification methods and strategies based on the considered model artifacts. Our research also provides a relevant contribution by identifying the major challenges in MDD and proposing relevant solutions.Source: THE JOURNAL OF SYSTEMS AND SOFTWARE, vol. 230
DOI: 10.1016/j.jss.2025.112508
Metrics:
Jadoon G., Ter Beek M. H., Ferrari A.
Rigorous software development involves using highly structured methods and processes in software and system engineering to ensure that the developed products are correct, reliable, and robust. In this context, model-driven development (MDD) has emerged as a development paradigm that emphasizes designing software systems by means of graphical or textual models at different levels of abstraction, which capture different aspects or dimensions of the system-to-be. At the core of MDD is model transformation, which is the process of translating one model into another, according to specific rules. Property preservation in MDD refers to maintaining specific properties of the system model during transformations, including structural, behavioral, and domain-specific constraints. Over the past decades, research on model transformation and property preservation has seen several contributions. In this paper, we present a systematic literature review (SLR) to compile information on study demographics, model properties considered, techniques to ensure property preservation, and other aspects. In addition, through thematic analysis, we highlight significant challenges and benefits associated with model transformation and property preservation. We analyze 182 research studies published between 2000 and 2024. Most of the studies concern case studies (52) and rigorous analysis (47), while experimental studies using human subjects are limited (1). Formal logic is the most commonly used transformation language, used in 35 studies, while the Unified Modeling Language (UML) is also used for source (55) and target (21) modeling. A total of 93 of the studies performed system testing on models, while 44 of the studies used transformation rules to verify transformation properties. Among the verified model properties, 64 studies focused on consistency management, while 4 are related to model maintainability and reuse. We conclude from our SLR that property preservation could be improved by using model-specific verification methods and strategies based on the considered model artifacts. Our research also provides a relevant contribution by identifying the major challenges in MDD and proposing relevant solutions.Source: THE JOURNAL OF SYSTEMS AND SOFTWARE, vol. 230
DOI: 10.1016/j.jss.2025.112508
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CNR IRIS 
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2025
Other
Open Access
Model transformation and property preservation in rigorous software development
Jadoon Gullelala
Model-driven development (MDD) is an integral methodology for developing complex software systems, where model transformation plays a vital role in enhancing and modifying these models. However, ensuring consistent preservation of desired characteristics during these transformations remains a significant challenge, leading to potential inconsistencies and deficiencies in the final system. This research aims to address this challenge by introducing a novel Property Preservation Framework (PPF) that focuses on preserving both functional and non-functional properties during model transformations. We also propose a framework for preserving non-functional requirements (NFRs) in goal models, using meta-models of the software product lines (SPL). Through a systematic literature review and the analysis of several research studies published between 2000 and 2024, this research identifies the major challenges and benefits of model transformation and property preservation. Most of the studies concern case studies (52) and rigorous analysis (47), while experimental studies using human subjects are limited (1). Formal logic is the most commonly used transformation language, used in 35 studies, while the Unified Modeling Language (UML) is also used for source (55) and target (21) modeling. A total of 93 of the studies performed system testing on models, while 44 of the studies used transformation rules to verify transformation properties. Among the verified model properties, 64 studies focused on consistency management, while 4 are related to model maintainability and reuse. Additionally, it highlights the significance of model testing and formal verification techniques in ensuring the preservation of model properties. The PPF integrates the application of AI methodologies, constraint-checking strategies, and model validation mechanisms into the model transformation workflow. By prioritizing property specification, verification, and preservation, the framework facilitates the identification and rectification of property violations at multiple transformation stages. This systematic approach significantly enhances overall consistency and reliability, amplifying model precision and dependability. NFRs are fundamental in SPL engineering, however, preserving NFRs across product variants induces considerable challenges, particularly in goal-oriented SPLE where goals guide product derivation. Our proposed framework serves 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. In conclusion, this research highlights the significance of effective model transformation and preservation strategies. Offering comprehensive frameworks for the preservation of essential features contributes substantially to resolving significant challenges within the MDD process, ultimately ensuring the development of accurate and reliable models.
Jadoon Gullelala
Model-driven development (MDD) is an integral methodology for developing complex software systems, where model transformation plays a vital role in enhancing and modifying these models. However, ensuring consistent preservation of desired characteristics during these transformations remains a significant challenge, leading to potential inconsistencies and deficiencies in the final system. This research aims to address this challenge by introducing a novel Property Preservation Framework (PPF) that focuses on preserving both functional and non-functional properties during model transformations. We also propose a framework for preserving non-functional requirements (NFRs) in goal models, using meta-models of the software product lines (SPL). Through a systematic literature review and the analysis of several research studies published between 2000 and 2024, this research identifies the major challenges and benefits of model transformation and property preservation. Most of the studies concern case studies (52) and rigorous analysis (47), while experimental studies using human subjects are limited (1). Formal logic is the most commonly used transformation language, used in 35 studies, while the Unified Modeling Language (UML) is also used for source (55) and target (21) modeling. A total of 93 of the studies performed system testing on models, while 44 of the studies used transformation rules to verify transformation properties. Among the verified model properties, 64 studies focused on consistency management, while 4 are related to model maintainability and reuse. Additionally, it highlights the significance of model testing and formal verification techniques in ensuring the preservation of model properties. The PPF integrates the application of AI methodologies, constraint-checking strategies, and model validation mechanisms into the model transformation workflow. By prioritizing property specification, verification, and preservation, the framework facilitates the identification and rectification of property violations at multiple transformation stages. This systematic approach significantly enhances overall consistency and reliability, amplifying model precision and dependability. NFRs are fundamental in SPL engineering, however, preserving NFRs across product variants induces considerable challenges, particularly in goal-oriented SPLE where goals guide product derivation. Our proposed framework serves 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. In conclusion, this research highlights the significance of effective model transformation and preservation strategies. Offering comprehensive frameworks for the preservation of essential features contributes substantially to resolving significant challenges within the MDD process, ultimately ensuring the development of accurate and reliable models.
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flore.unifi.it | CNR IRIS | CNR IRIS
2024
Conference article
Restricted
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
Metrics:
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
Metrics:
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
Metrics:
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
Metrics:
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IEEE Access | IEEE Access | CNR IRIS | ieeexplore.ieee.org | CNR IRIS