Different Models for Model Matching: An Analysis of Approaches to Support Model

Calculating differences between models is an important and challenging task in Model Driven Engineering. Model differencing involves a number of steps starting with identifying matching model elements, calculating and representing their differences, and finally visualizing them in an appropriate way. In this paper, we provide an overview of the fundamental steps involved in the model differencing process and summarize the advantages and shortcomings of existing approaches for identifying matching model elements. To assist potential users in selecting one of the existing methods for the problem at stake, we investigate the trade-offs these methods impose in terms of accuracy and effort required to implement each one of them.

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Different models for model matching: An analysis of approaches to support model differencing

In model-based software engineering, models are primary artifacts which iteratively evolve and which are often developed in teams. Therefore, comparison and merge tools for models are indispensable. These tools must compare models in a technology-dependent runtime representation and will initially derive low-level changes, which can differ considerably from user-level editing commands. Low-level differences are often incomprehensible and should be semantically lifted to the level of editing operations. This transformation of differences depends on the model type, supported editing operations, and user preferences; thus specific transformers are needed, and building them is a challenge. We present a rule-based approach to this problem: low-level differences are represented based on the Eclipse Modeling Framework. They are transformed into representations of editing operations using a rule-based model transformation engine. The necessary transformation rules are automatically derived from basic transformation rules for the editing operations.

https://www.mathematik.uni-marburg.de/~swt/ws12/mdd/files/KKT11.pdf

A rule-based approach to the semantic lifting of model differences in the context of model versioning

This survey paper presents the current state of model comparison as it applies to Model-Driven Engineering. We look specifically at how model matching is accomplished, the application of the approaches, and the types of models that approaches are intended to work with. Our paper also indicates future trends and directions. We find that many of the latest model comparison techniques are geared towards facilitating arbitrary meta models and use similarity-based matching. Thus far, model versioning is the most prevalent application of model comparison. Recently, however, work on comparison for versioning has begun to stagnate, giving way to other applications. Lastly, there is wide variance among the tools in the amount of user effort required to perform model comparison, as some require more effort to facilitate more generality and expressive power.

/pdf/a-survey-of-model-comparison-approaches-and-applications-yysly3i093.pdf

A Survey of Model Comparison Approaches and Applications

Testing is essential for quality assurance of database applications. Achieving high code coverage of the database application is important in testing. In practice, there may exist a copy of live databases that can be used for database application testing. Using an existing database state is desirable since it tends to be representative of real-world objects' characteristics, helping detect faults that could cause failures in real-world settings. However, to cover a specific program code portion (e.g., block), appropriate program inputs also need to be generated for the given existing database state. To address this issue, in this paper, we propose a novel approach that generates program inputs for achieving high code coverage of a database application, given an existing database state. Our approach uses symbolic execution to track how program inputs are transformed before appearing in the executed SQL queries and how the constraints on query results affect the application's execution. One significant challenge in our problem context is the gap between program-input constraints derived from the program and from the given existing database state; satisfying both types of constraints is needed to cover a specific program code portion. Our approach includes novel query formulation to bridge this gap. Our approach is loosely integrated into Pex, a state-of-the-art white-box testing tool for .NET from Microsoft Research. Empirical evaluations on two real database applications show that our approach assists Pex to generate program inputs that achieve higher code coverage than the program inputs generated by Pex without our approach's assistance.

/pdf/generating-program-inputs-for-database-application-testing-pco06qf90e.pdf

Generating program inputs for database application testing

For long, fine-grained version control for software documents has been neglected severely. Typically, software configuration management systems support the management of text or binary files. Unfortunately, text-based tools for fine-grained version control are not adequate for software documents produced in earlier phases in the software life cycle. Frequently, these documents have a graphical syntax; therefore we will call them software diagrams. This paper discusses the current state of the art in fine-grained version control (differencing and merging) for software diagrams with an emphasis on UML diagrams.

/pdf/differencing-and-merging-of-software-diagrams-state-of-the-7wjwxgi2pc.pdf

DIFFERENCING AND MERGING OF SOFTWARE DIAGRAMS - State of the Art and Challenges

Bei der Differenzberechnung auf Diagrammen nach dem mengenorientierten Ansatz [3] wird ein Durchschnitt vorausgesetzt, auf den sich die Unterschiede beziehen. Verfahren, die keine eindeutigen Objektbezeichner voraussetzen, bestimmen korrespondierende Elemente uber verschiedene Heuristiken. Die Definition einer Abstandsmetrik auf Diagrammen kann ein objektives Mas bereitstellen, um die Qualitat der berechneten Differenzen zu beurteilen. Steht eine Abstandsmetrik zur Verfugung, kann die Differenzberechnung auf Diagrammen als Optimierungsproblem betrachtet werden, eine Losung mit minimalem Abstand zu finden.

https://pi.informatik.uni-siegen.de/stt/27_2/SE2007/Foertsch_2007_sttVVUM07.pdf

Sabrina Förtsch

Papers

DIFFERENCING AND MERGING OF SOFTWARE DIAGRAMS - State of the Art and Challenges

Differenzberechnung auf Diagrammen mittels Editierabstand.