A Meta-Review on Artiﬁcial Intelligence in Product Creation - Bernijazov, Ruslan; Dicks, Alexander; Dumitrescu, Roman; Foullois, Marc; Hanselle, Jonas Manuel; Hüllermeier, Eyke; Karakaya, Gökce; Ködding, Patrick; Lohweg, Volker; Malatyali, Manuel; Meyer auf der Heide, Friedhelm; Panzner, Melina; et al. (Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21))
Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. Im Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden.
Keywords: Artificial Intelligence Product Creation Literature Review

Quality Assurance with Dynamic Meta Modeling - Soltenborn, Christian
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 universally 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.

Coverage Criteria for Testing DMM Specifications - Arifulina, Svetlana; Engels, Gregor; Soltenborn, Christian (Proceedings of the 11th International Workshop on Graph Transformation and Visual Modeling Techniques (GT-VMT))
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 ofDMM 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.

QUAASY - QUality Assurance of Adaptive SYstems - Luckey, Markus; Gerth, Christian; Soltenborn, Christian; Engels, Gregor (Proceedings of the 8th International Conference on Autonomic Computing (ICAC'11))

Extending DMM Behavior Specifications for Visual Execution and Debugging - Bandener, Nils; Soltenborn, Christian; Engels, Gregor (Proceedings of the 3rd International Conference on Software Language Engineering (SLE 2010))

Using rule overriding to improve reusability and understandability of Dynamic Meta Modeling specifications - Soltenborn, Christian; Engels, Gregor (Journal of Visual Languages & Computing)
Dynamic Meta Modeling (DMM) is a visual semantics specification technique targeted at languages based on a metamodel. A DMM specification consists of a runtime metamodel and operational rules which describe how instances of the runtime metamodel change over time. A known deficiency of the DMM approach is that it does not support the refinement of a DMM specification, e.g., in the case of defining the semantics for a refined and extended domain-specific language (DSL). Up to now, DMM specifications could only be reused by adding or removing DMM rules. In this paper, we enhance DMM such that DMM rules can override other DMM rules, similar to a method being overridden in a subclass, and we show how rule overriding can be realized with the graph transformation tool GROOVE. We argue that rule overriding does not only have positive impact on reusability, but also improves the intuitive understandability of DMM semantics specifications.

Showing Full Semantics Preservation in Model Transformation - A Comparison of Techniques - Hülsbusch, Mathias; König, Barbara; Rensink, Arend; Semenyak, Maria; Soltenborn, Christian; Wehrheim, Heike (Integrated Formal Methods - 8th International Conference, IFM 2010, Nancy, France, October 11-14, 2010. Proceedings)

Full Semantics Preservation in Model Transformation - A Comparison of Proof Techniques - Hülsbusch, Mathias; König, Barbara; Rensink, Arend; Semenyak, Maria; Soltenborn, Christian; Wehrheim, Heike

Test-driven Language Derivation with Graph Transformation-based Dynamic Meta Modeling - Engels, Gregor; Soltenborn, Christian (Proceedings of the International Colloquium on Graph and Model Transformation (GraMoT 2010), Berlin (Germany))
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.

Towards Test-Driven Semantics Specification - Soltenborn, Christian; Engels, Gregor (Proceedings of the 12th International Conference on Model Driven Engineering Languages and Systems (MODELS 2009), Denver, Colorado (USA))
Behavioral models are getting more and more important within the software development cycle. To get the most use out of them, their behavior should be defined formally. As a result, many approaches exist which aim at specifying formal semantics for behavioral languages (e.g., Dynamic Meta Modeling (DMM), Semantic Anchoring). Most of these approaches give rise to a formal semantics which can e.g. be used to check the quality of a particular language instance, for instance using model checking techniques. However, if the semantics specification itself contains errors, it is more or less useless, since one cannot rely on the analysis results. Therefore, the language engineer must make sure that the semantics he develops is of the highest quality possible. To help the language engineer to achieve that goal, we propose a test-driven semantics specification process: the semantics of the language under consideration is first informally demonstrated using example models, which will then be used as test cases during the actual semantics specification process. In this paper, we present this approach using the already mentioned specification language DMM.