Using Benji to systematically evaluate model comparison algorithms
2020 (English)In: Proceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings, Association for Computing Machinery, Inc , 2020, p. 56-60Conference paper, Published paper (Refereed)
Abstract [en]
Model comparison is a critical task in model-driven engineering. Its correctness enables an effective management of model evolution, synchronisation, and even other tasks, such as model transformation testing. The literature is rich as concerns comparison algorithms approaches, however the same cannot be said for their systematic evaluation. In this paper we present Benji, a tool for the generation of model comparison benchmarks. In particular, Benji provides domain-specific languages to design experiments in terms of input models and possible manipulations, and based on those generates corresponding benchmark cases. In this way, the experiment specification can be exploited as a systematic way to evaluate available comparison algorithms against the problem under study.
Place, publisher, year, edition, pages
Association for Computing Machinery, Inc , 2020. p. 56-60
Keywords [en]
Model comparison algorithms, Model comparison benchmarks, Model differencing, Model evolution, Systematic evaluation, Engineering, Industrial engineering, Design experiments, Domain specific languages, Effective management, Model comparison, Model transformation, Model-driven Engineering, Problem oriented languages
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-52787DOI: 10.1145/3417990.3422009Scopus ID: 2-s2.0-85096742136ISBN: 9781450381352 (print)OAI: oai:DiVA.org:mdh-52787DiVA, id: diva2:1508587
Conference
23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020; Virtual, Online; Canada; 16 October 2020 through 23 October 2020
Note
Export Date: 10 December 2020; Conference Paper
2020-12-102020-12-102020-12-10Bibliographically approved