Mirador

Barrett, Stephen C.; Butler, Gregory; Chalin, Patrice

doi:10.1145/1826147.1826151

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…[159], Kehrer and Kelter [73], and Barrett et al [17] all use configurable matching, which means that any matching technique can be used as long as it fulfills specific criteria. Badreddin et al [16], along with van Rozen and van der Storm [157], use raw text differencing in some form.…”

Section: Matching Techniques (Rq12)mentioning

confidence: 99%

Systematic review of matching techniques used in model-driven methodologies

Somogyi

Asztalos

2019

Softw Syst Model

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In model-driven methodologies, model matching is the process of finding a matching pair for every model element between two or more software models. Model matching is an important task as it is often used while differencing and merging models, which are key processes in version control systems. There are a number of different approaches to model matching, with most of them focusing on different goals, i.e., the accuracy of the matching process, or the generality of the algorithm. Moreover, there exist algorithms that use the textual representations of the models during the matching process. We present a systematic literature review that was carried out to obtain the state-of-the-art of model matching techniques. The search process was conducted based on a well-defined methodology. We have identified a total of 3274 non-duplicate studies, out of which 119 have been included as primary studies for this survey. We present the state-of-the-art of model matching, highlighting the differences between different matching techniques, mainly focusing on text-based and graph-based algorithms. Finally, the main open questions, challenges, and possible future directions in the field of model matching are discussed, also including topics like benchmarking, performance and scalability, and conflict handling. Keywords Model matching • Model comparison • Model differencing • Version control • Text-based modeling • Systematic literature review Communicated by Professor Jon Whittle.

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Section: Matching Techniques (Rq12)mentioning

confidence: 99%