Model-Driven Development of Complex Software: A Research Roadmap (1409.6620v1)

Abstract: The term Model-Driven Engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. In this paper we give an overview of current research in MDE and discuss some of the major challenges that must be tackled in order to realize the MDE vision of software development. We argue that full realizations of the MDE vision may not be possible in the near to medium-term primarily because of the wicked problems involved. On the other hand, attempting to realize the vision will provide insights that can be used to significantly reduce the gap between evolving software complexity and the technologies used to manage complexity.

Model-driven Development of Complex Software: A Research Roadmap

The paper "Model-driven Development of Complex Software: A Research Roadmap" by Robert France and Bernhard Rumpe provides a comprehensive overview of the contemporary research landscape and challenges in Model-Driven Engineering (MDE). The authors systematically dissect the intricate aspects of MDE, presenting both the potential and the roadblocks in the field.

The paper pivots around a core vision of MDE, which aspires to elevate software development from manual code-centric efforts to model-centric approaches. This paradigm shift is driven by the escalating complexity of modern software systems, which the authors argue cannot be efficiently managed through traditional programming techniques alone. The manifesto of MDE is to harness abstract models of systems, transforming these models systematically into executable software with reduced human intervention.

Key Elements and Challenges in MDE

France and Rumpe highlight several pivotal challenges within MDE, categorized as follows:

1. Modeling Language Challenges:
   • The abstraction challenge focuses on creating modeling languages that effectively encapsulate problem-level abstractions.
   • The formality challenge addresses the need for formal semantics in modeling languages to support reliable model manipulation.
2. Separation of Concerns:
   • Addressing crosscutting concerns and interactions among multiple viewpoints is critical. The paper details approaches such as Aspect-Oriented Modeling (AOM) to manage these complexities.
3. Model Manipulation and Management:
   • The paper emphasizes the need for advanced tooling to support model transformation, consistency maintenance, traceability, versioning, and runtime model utilization.

These challenges collectively present a daunting landscape, but France and Rumpe propose that addressing even incremental parts of these challenges can yield significant advancements in the state of software engineering.

Numerical Results and Evidence

The authors do not provide direct numerical results in the traditional sense of empirical research but underscore several conceptual frameworks and existing tools. For instance, they mention tools like Compuware’s OptimalJ and IBM’s Rational XDE that are pioneering the automation of significant parts of the software lifecycle. Their discussions imply that industry tools are making strides but are still far from fulfilling the comprehensive MDE vision.

Bold Claims and Implications

A bold claim discussed is that while the full realization of MDE's vision might be distant due to its inherent complexity and the "wicked problems" involved, the pursuit itself will foster insights that bridge the gap between evolving software complexity and its management technologies. These "wicked problems" encompass multifaceted and interrelated technical and social issues, suggesting that solutions developed will inevitably lead to advancements in understanding and managing software complexity.

Practical and Theoretical Implications

Practically, the advancement of MDE technologies has implications for how future software systems are developed, deployed, and maintained. The transformation from abstract models to execution-level artifacts promises to reduce development time and costs while enhancing the quality and robustness of the software.

Theoretically, the progress in MDE will enrich the body of knowledge regarding model semantics, model transformations, and their formal verification. This knowledge has the potential to converge diverse strands of software engineering research, from formal methods to compiler techniques and systematic reuse.

Speculating on Future Developments

Future advancements in AI coupled with MDE will likely catalyze the creation of more intelligent, adaptive software systems. These systems could leverage runtime models extensively, enabling self-monitoring and automated adaptation in real-time. AI-driven model evolution and refinement could also become a staple, facilitating continuous integration and delivery pipelines that significantly outpace current methodologies.

Conclusion

France and Rumpe's roadmap for MDE research is both ambitious and grounded in a realistic assessment of current capabilities and future needs. By emphasizing the challenges while also hinting at the transformative potential of MDE, the paper strategically positions this domain as essential for the evolution of software engineering. Their vision, although idealistic, sets a robust framework for ongoing and future research, aiming for incremental yet impactful advancements towards fully model-driven software production environments.