Another significant result is that also people not perfectly trained on our method and environment may benefit from their use: the training phase could be shortened or skipped. Practice: One of the most interesting practical implications of our study is - in case the time difference is not an issue, but moving people might be a problem, the new method and environment could represent a viable alternative to face-to-face. Results: The results on participants' familiarity indicate a significant effect on the task completion time (i.e., more familiar participants spent less time), with no significant impact on quality. The results show a significant difference in favor of face-to-face (i.e., the chosen baseline) for the time to complete modeling tasks, with no significant impact on the quality of the produced models. To study the effect of the participants' familiarity with the method and the integrated environment, the second experiment is a replication conducted with the same participants as the original experiment. Methods: This new method has been implemented within an integrated environment, which allows distributed synchronous modeling and communication among team members. Objective: In this paper, we present the results of two controlled experiments conducted to assess a new method based on think-pair-square in the distributed modeling of use case diagrams. 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. 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. We have identified a total of 3274 non-duplicate studies, out of which 119 have been included as primary studies for this survey. The search process was conducted based on a well-defined methodology. We present a systematic literature review that was carried out to obtain the state-of-the-art of model matching techniques. Moreover, there exist algorithms that use the textual representations of the models during the matching process. 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. Model matching is an important task as it is often used while differencing and merging models, which are key processes in version control systems.
#Subversion conflict resolution software#
In model-driven methodologies, model matching is the process of finding a matching pair for every model element between two or more software models.
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