Approaches to Co-Evolution of Metamodels and Models: A Survey

Hebig, Regina; Khelladi, Djamel Eddine; Bendraou, Reda

doi:10.1109/tse.2016.2610424

Cited by 60 publications

(27 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model we described above and presented in Figure 3 is depicted in Figure 5. The classes 2 At a first glance it might seem that we're just using an alternative way of specifying a class diagram. However, using a specialized XML format has several advantages.…”

Section: The Outsystems Languagementioning

confidence: 99%

LUV is not the answer

Lourenço¹,

Tavares²,

Eugénio³

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

View full text Add to dashboard Cite

The OutSystems Platform is a visual model-driven development and delivery platform that allows developers to create enterprise-grade cross platform web and mobile applications. The platform consists of several interdependent components, most notably Service Studio, the Platform Server, and LifeTime. Service Studio is an integrated development environment used to create applications that are then compiled by the Platform Server. LifeTime is used to stage applications between different environments (e.g., development, testing, production). Our meta-model is versioned using a version number that we call Last Upgrade Version (LUV). Service Studio, the Platform Server, and the models they create/process are associated with a particular LUV. As a general rule, a platform component is only able to process models with the same LUV as the component itself. This approach is not very flexible: a change to the meta-model requires releasing a new set of platform components that our customers then need to install. Although there's low resistance to installing new versions of Service Studio, the same is not true for the Platform Server. Thus, for all practical purposes LUV changes are tied to releases of major versions of the OutSystems Platform. In this paper we share the techniques that allowed us to transition to a Continuous Delivery process in which our meta-model can evolve freely with no impact on our installed base. CCS CONCEPTS • Software and its engineering → Model-driven software engineering.

show abstract

Section: The Outsystems Languagementioning

confidence: 99%

LUV is not the answer

Lourenço¹,

Tavares²,

Eugénio³

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

View full text Add to dashboard Cite

show abstract

“…However, coevolution approaches repair a particular type of inconsistencies w.r.t. metamodels conformance [19,20]. Here we focus on model repair.…”

Section: Related Workmentioning

confidence: 99%

Detecting and exploring side effects when repairing model inconsistencies

Khelladi

Kretschmer²,

Egyed³

2019

Proceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering

Self Cite

View full text Add to dashboard Cite

When software models change, developers often fail in keeping them consistent. Automated support in repairing inconsistencies is widely addressed. Yet, merely enumerating repairs for developers is not enough. A repair can as a side effect cause new unexpected inconsistencies (negative) or even fix other inconsistencies as well (positive). To make matters worse, repairing negative side effects can in turn cause further side effects. Current approaches do not detect and track such side effects in depth, which can increase developers' effort and time spent in repairing inconsistencies. This paper presents an automated approach for detecting and tracking the consequences of repairs, i.e. side effects. It recursively explores in depth positive and negative side effects and identifies paths and cycles of repairs. This paper further ranks repairs based on side effect knowledge so that developers may quickly find the relevant ones. Our approach and its tool implementation have been empirically assessed on 14 case studies from industry, academia, and GitHub. Results show that both positive and negative side effects occur frequently. A comparison with three versioned models showed the usefulness of our ranking strategy based on side effects. It showed that our approach's top prioritized repairs are those that developers would indeed choose. A controlled experiment with 24 participants further highlights the significant influence of side effects and of our ranking of repairs on developers. Developers who received side effect knowledge chose far more repairs with positive side effects and far less with negative side effects, while being 12.3% faster, in contrast to developers who did not receive side effect knowledge. CCS Concepts • Software and its engineering → Model-driven software engineering; Software maintenance tools.

show abstract

“…Authors in [39] defined model migration as a development activity in which models are updated in response to metamodel evolution to re-establish conformance. The majority of research teams [47][48][49][50]65,66] have tackled the phenomenon of migration based on the M2/M1 architecture layers of MDE, where the authors presented a semi-automatic process to co-evolve model-to-model transformations upon meta-model evolution. The use of higher-order model transformations for processing meta-model evolution and model co-evolution is presented in [62,67] .…”

Section: Meta-model Matching Approachesmentioning

confidence: 99%

A scalable model based approach for data model evolution: Application to space missions data models

Oubelli

Aït‐Ameur

Bedouet

et al. 2018

Computer Languages, Systems & Structures

View full text Add to dashboard Cite

Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible a b s t r a c t During the development of a complex system, data models are the key to a successful engineering process, as they contain and organize all the information manipulated by the different functions involved in the design of the system. Moreover, these data models evolve throughout the design, as the development raises issues that have to be solved through a restructuration of data organization. But any such data model evolution has a deep impact on the functions that have already being defined.Recent research tries to deal with this issue by studying how complex industrial data models evolve from one version to another and how their data instances co-evolve. Complexity and scalability issues make this problem a major scientific challenge, leading to huge gains in development efficiency. This problem is of particular interest in the field of aeronautics and space systems. Indeed, the development of these systems produces many complex data models associated to the designed systems and/or to the systems under design, hence on the one hand data models are available. On the other hand, it is well known that these systems are developed in the context of collaborative projects that may last for decades. In such projects, specifications together with the associated data models are bound to evolve and engineering processes shall take into account this evolution.Our work addresses the problem of data model evolution in a model-driven engineering setting. We focus on minimizing the impact of model evolution on the system development processes in the specific context on the space engineering area, where data models may involve thousands of concepts and relationships, and we investigate the performance of the model-based development (MBD) approach we propose for data model evolution over two space missions, namely PHARAO and MICROSCOPE.

show abstract

Approaches to Co-Evolution of Metamodels and Models: A Survey

Cited by 60 publications

References 61 publications

LUV is not the answer

LUV is not the answer

Detecting and exploring side effects when repairing model inconsistencies

A scalable model based approach for data model evolution: Application to space missions data models

Contact Info

Product

Resources

About