Software systems are subject to series of changes during their evolution as they move from one release to the next. The change histories of software systems hold useful information that describes how artifacts evolved. Evolution-based metrics, which are the means to quantify the change history, are potentially good indicators of the changes in a software system. The objective of this paper is to derive and validate (theoretically and empirically) a set of evolution-based metrics as potential indicators of the change-prone classes of an objectoriented system when moving from one release to the next. Release-by-release statistical prediction models were built in different ways. The results indicate that the proposed evolution-based metrics measure different dimensions from those of typical product metrics. Additionally, several evolution-based metrics were found to be correlated with the change-proneness of classes. Moreover, the results indicate that more accurate prediction of class change-proneness is achieved when the evolution-based metrics are combined with product metrics.

Abstract-Model matching is at the core of different model management operations such as model evolution, consolidation, and retrieval. An accurate identification of the similarity and differences between the elements of the matched models leads to an accurate model matching, which, in turn, leads to better model management. Software metrics are the software engineer means to quantify the similarity between the elements of the matched models. In this paper, we empirically validate the use of different metrics for capturing the similarity and the differences between the elements of two matched UML class diagrams. The paper empirically investigates the improvement of the similarity assessment of the class diagrams through the weight calibration of compound metrics. The results, reported based on two case studies, show the superiority of the compound metrics over the individual metrics.Index Terms-Model matching, similarity metrics, reuse, weight calibration.

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