Passer au contenu

/ Département d'informatique et de recherche opérationnelle

Je donne


Navigation secondaire

Soutenance de thèse - Manouchehr Zadahmad Jafarlou

Bonjour à tous,

Vous êtes cordialement invité.e.s à la southenance de thèse de Manouchehr Zadahmad Jafarlou, le 27 novembre à 10h00. (Présentation hybride).

Title: Domain-Specific Differencing and Merging of Models

Date: 27 Novembre 2023 de 10:00 à 12:00 EST

Location: AA-3195 - 2920 Ch de la Tour, Montréal



Président rapporteur
Famelis, Michalis
Directeur de rechercheSyriani, Eugene
Membre régulier
Hafid, Abdelhakim Senhaji
Examinateur externe
Kolovos, Dimitris
Représentant du doyen
à communiquer



In the context of collaborative software engineering, version control systems (VCS) play a crucial role in managing code changes, promoting  collaboration, and ensuring the integrity of shared projects. This  significance extends to model-driven engineering (MDE), where domain  experts design domain-specific models (DSM). In this context,  collaborating with VCS aids in coordinating model changes and preserving the integrity of DSMs. However, existing solutions primarily focus on generic approaches, considering models as generic text. VCS report the differences between model versions in an abstract and unintuitive way for domain experts. This also poses challenges when resolving conflicts  and merging models, adding complexity to the workflow of domain experts.


The goal of this thesis is to provide domain-specific VCS for domain experts, focusing on the two main components of VCS, namely differencing and merging. We introduce DSMCompare, a domain-specific model comparison tool integrated with three-way conflict detection, resolution, and merging capabilities. DSMCompare provides concise representations of differences and conflicts at different levels of granularity, while using the graphical syntax of the original DSMs. In our evaluations, DSMCompare demonstrated significant improvements over generic differencing and merging solutions, including a reduction in reported difference verbosity, differences expressed using the semantics of the domain, accurate detection of semantic differences and conflicts between different versions of a model, correct conflict resolution, a reduction in manual interactions needed, and an overall improvement in efficiency for domain experts.