Stress-testing centralised model stores

One of the current challenges in model-driven engineering is enabling effective collaborative modelling. Two common approaches are either storing the models in a central repository, or keeping them under a traditional file-based version control system and build a centralized index for model-wide queries. Either way, special attention must be paid to the nature of these repositories and indexes as networked services: they should remain responsive even with an increasing number of concurrent clients. This paper presents an empirical study on the impact of certain key decisions on the scalability of concurrent model queries, using an Eclipse Connected Data Objects model repository and a Hawk model index. The study evaluates the impact of the network protocol, the API design and the internal caching mechanisms and analyzes the reasons for their varying performance

Towards efficient loading of change-based models

This paper proposes and evaluates an efficient approach for loading models stored in a change-based format. The work builds on language-independent change-based persistence (CBP) of models conforming to object-oriented metamodelling architectures such as MOF and EMF, an approach which persists a model’s editing history rather than its current state. We evaluate the performance of the proposed loading approach and assess its impact on saving change-based models. Our results show that the proposed approach significantly improves loading times compared to the baseline CBP loading approach, and has a negligible impact on saving

Model Consistency for Distributed Collaborative Modeling

International audienceCurrent collaborative modeling tools use a centralized architecture , based on version control system, where models are updated asynchronously. These tools depend on a single server and are not completely adapted for collaborative modeling, where update reactivity is essential. In this paper, we propose a framework for building collabo-rative modeling tools which provides synchronous model update. The framework is based on a peer-to-peer architecture and uses a consistency algorithm for model updating

Neo4EMF, A Scalable Persistence Layer for EMF Models

International audienceSeveral industrial contexts require software engineering methods and tools able to handle large -size artifacts. The central idea of abstraction makes model-driven engineering (MDE) a promising approach in such contexts, but current tools do not scale to very large models (VLMs): already the task of storing and accessing VLMs from a persisting support is currently ine cient. In this paper we propose a scalable persistence layer for the de-facto standard MDE framework EMF. The layer exploits the e ciency of graph databases in storing and accessing graph structures, as EMF models are. A preliminary experimentation shows that typical queries in reverse-engineering EMF models have good performance on such persistence layer, compared to le-based backends

Preface

The aim of the Transformation Tool Contest (TTC) series is to compare the expressiveness,the usability, and the performance of transformation tools along a number of selected casestudies.  A deeper understanding of the relative merits of different tool features will help tofurther improve transformation tools and to indicate open problems.These proceedings gather the cases and solutions developed by the contest participants ofthe thirteenth and fourteenth editions. Both editions were part of the Software Technologies:Applications and Foundations (STAF) federation of conferences during 2020 and 2021. Teamsfrom the major international players in transformation tool development participated in anonline setting owing to the pandemic. Thus, these are the pandemic proceedings of the TTCseries.</p