Towards Generalizing Visual Process Patterns

Visual Process Patterns (VPP) is a visual language to describe constraints on the behavior of UML Activities. They have been developed for the sake of formulating and verifying requirements on business process models in a visual, intuitive way (with UML Activities being one possible description language). In the VPP approach, a visual process pattern is translated into an LTL formula, which can then be verified against a transition system describing the behavior of the Activity under consideration. In this paper, we aim at generalizing VPP. We show how to formulate patterns more generally, using an enhanced version of the concrete syntax of the behavioral model under consideration. Additionally, we describe how these more general patterns can be verified against a modelâs behavior

Test-driven Language Derivation with Graph Transformation-Based Dynamic Meta Modeling

Deriving a new language L_B from an already existing one L_A is a typical task in domain-specific language engineering. Here, besides adjusting L_A's syntax, the language engineer has to modify the semantics of L_A to derive L_B's semantics. Particularly, in case of behavioral modeling languages, this is a difficult and error-prone task, as changing the behavior of language elements or adding behavior for new elements might have undesired side effects. Therefore, we propose a test-driven language derivation process. In a first step, the language engineer creates example models containing the changed or newly added elements in different contexts. For each of these models, the language engineer also precisely describes the expected behavior. In a second step, each example model and its description of behavior is transformed into an executable test case. Finally, these test cases are used when deriving the actual semantics of L_B - at any time, the language engineer can run the tests to verify whether the changes he performed on L_A's semantics indeed produce the desired behavior. In this paper, we illustrate the approach using our graph transformation-based semantics specification technique Dynamic Meta Modeling. This is once more an example where the graph transformation approach shows its strengths and appropriateness to support software engineering tasks as, e.g., model transformations, software specifications, or tool development

Coverage Criteria for Testing DMM Specifications

Behavioral modeling languages are most useful if their behavior is specified formally such that it can e.g. be analyzed and executed automatically. Obviously, the quality of such behavior specifications is crucial. The rule-based semantics specification technique Dynamic Meta Modeling (DMM) honors this by using the approach of Test-driven Semantics Specification (TDSS), which makes sure that the specification at hand at least describes the correct behavior for a suite of test models. However, in its current state TDSS does not provide any means to measure the quality of such a test suite.In this paper, we describe how we have applied the idea of test coverage to TDSS. Similar to common approaches of defining test coverage criteria, we describe a data structure called invocation graph containing possible orders of applications of DMM rules. Then we define different coverage  criteria based on that data structure, taking the rule applications caused by the test suite’s models into account. Our implementation of the described approach gives the language engineer using DMM a means to reason about the quality of the language’s test suite, and also provides hints on how to improve that quality by adding dedicated test models to the test suite

Quality assurance with dynamic meta modeling

Dynamic Meta Modeling (DMM) ist eine Semantikbeschreibungstechnik, die sich auf MOF-basierte Sprachen fokussiert und deren Verhalten durch graphische, operationale Regeln beschreibt. Der DMM-Ansatz wurde im Jahr 2000 von Engels et al. erstmals beschrieben und von Hausmann in 2006 in seiner Dissertation ausgearbeitet. Der nächste Schritt war nun, an verschiedenen Modellierungssprachen zu erproben, um die gemachten Erfahrungen in die Verbesserung von DMM und seinen Werkzeugen einfließen zu lassen. Das Ergebnis ist die DMM++-Methode, die in dieser Arbeit vorgestellt wird. Wir haben vorwiegend an drei Stellen Verbesserungen vorgenommen: Erstens haben wir basierend auf unseren Erfahrungen mit DMM neue Sprachkonzepte wie die Verfeinerung von Regeln entwickelt, und wir haben bestehende Konzepte wie die Behandlung von universell quantifizierten Strukturen oder Attributen verbessert. Zweitens haben wir einen testgetriebenen Semantikspezifizierungsprozess entwickelt: Zunächst wird eine Menge von Beispielmodellen erzeugt und deren erwartetes Verhalten formalisiert. Die DMM-Regeln werden dann inkrementell entwickelt, wobei geprüft wird, ob die Beispielmodelle tatsächlich das erwartete Verhalten erzeugen. Zudem haben wir Abdeckungskriterien für Tests von DMM-Spezifikationen entwickelt, die die Beurteilung der Qualität der Tests erlauben. Drittens haben wir gezeigt, wie funktionale und nichtfunktionale Anforderungen an Modelle und ihre DMM-Spezifikation formuliert und geprüft werden können. Für ersteres haben wir eine graphische Sprache zur Formulierung temporallogischer Eigenschaften zur Verfügung gestellt, die dann mit Model Checking geprüft werden. Für zweiteres ermöglichen wir dem Modellierer das Hinzufügen von Performanceinformationen zu den Modellen, aufgrund dessen dann z.B. der average throughput eines Modells berechnet werden kann.Dynamic Meta Modeling (DMM) is a semantics specification technique targeted at MOF-based modeling languages, where a language's behavior is defined by means of graphical operational rules which change runtime models. The DMM approach has first been suggested by Engels et al. in 2000; Hausmann has then defined the DMM language on a conceptual level within his PhD thesis in 2006. Consequently, the next step was to bring the existing DMM concepts alive, and then to apply them to different modeling languages, making use of the lessons learned to improve the DMM concepts as well as the DMM tooling. The result of this process is the DMM++ method, which is presented within this thesis. Our contributions are three-fold: First, and according to our experiences with the DMM language, we have introduced new concepts such as refinement by means of rule overriding, and we have strengthened existing concepts such as the dealing with universal quantified structures or attributes. Second, we have developed a test-driven process for semantics specification: A set of test models is created, and their expected behavior is fixed. Then, the DMM rules are created incrementally, finally resulting in a DMM ruleset realizing at least the expected behavior of the test models. Additionally, we have defined a set of coverage criteria for DMM rulesets which allow to measure the quality of a set of test models. Third, we have shown how functional as well as non-functional requirements can be formulated against models and their DMM specifications. The former is achieved by providing a visual language for formulating temporal logic properties, which are then verified with model checking techniques, and by allowing for visual debugging of models failing a requirement. For the latter, the modeler can add performance information to models and analyze their performance properties, e.g. average throughput.Tag der Verteidigung: 04.07.2013Paderborn, Univ., Diss., 201

Full Semantics Preservation in Model Transformation – A Comparison of Proof Techniques

Model transformation is a prime technique in modern, model-driven software design. One of the most challenging issues is to show that the semantics of the models is not affected by the transformation. So far, there is hardly any research into this issue, in particular in those cases where the source and target languages are different.\ud \ud In this paper, we are using two different state-of-the-art proof techniques (explicit bisimulation construction versus borrowed contexts) to show bisimilarity preservation of a given model transformation between two simple (self-defined) languages, both of which are equipped with a graph transformation-based operational semantics. The contrast between these proof techniques is interesting because they are based on different model transformation strategies: triple graph grammars versus in situ transformation. We proceed to compare the proofs and discuss scalability to a more realistic setting.\u

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This thesis is dedicated to my family. Thanks for always supporting me, no matter which way I chose..

Showing Full Semantics Preservation in Model Transformation - A Comparison of Techniques

Model transformation is a prime technique in modern, model-driven software design. One of the most challenging issues is to show that the semantics of the models is not affected by the transformation. So far, there is hardly any research into this issue, in particular in those cases where the source and target languages are different.\ud \ud In this paper, we are using two different state-of-the-art proof techniques (explicit bisimulation construction versus borrowed contexts) to show bisimilarity preservation of a given model transformation between two simple (self-defined) languages, both of which are equipped with a graph transformation-based operational semantics. The contrast between these proof techniques is interesting because they are based on different model transformation strategies: triple graph grammars versus in situ transformation. We proceed to compare the proofs and discuss scalability to a more realistic setting.\u