Model transformation and property preservation in rigorous software development

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.