Model transformation and property preservation in rigorous software development: a systematic literature review

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.