Code Generation for UML 2 Activity Diagrams

Gessenharter, Dominik; Rauscher, Martin

doi:10.1007/978-3-642-21470-7_15

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Gessenharter and Rauscher [9] presented a code generation approach for UML class diagrams and UML ADs. For the structural part, their approach generates Java code from class diagrams comprising classes, attributes, and associations.…”

Section: Code Generation From Adsmentioning

confidence: 99%

Exhaustive Simulation and Test Generation Using fUML Activity Diagrams

Iqbal

Ashraf

Truşcan

et al. 2019

Advanced Information Systems Engineering

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The quality of the specifications used for test generation plays an important role in the quality of the generated tests. One approach to improve the quality of the UML specification is the use of executable models specified using the Foundational Subset for Executable UML Models (fUML) and the Action language for fUML (Alf). Due to their precise semantics, fUML and Alf models can be simulated or executed using an fUML execution engine. However, in order to execute the models exhaustively, one must provide input data required to reach and cover all essential elements not only in the graphical fUML models, but also in the textual Alf code associated with the graphical models. In this paper, we present an approach for exhaustive simulation and test generation from fUML activity diagrams containing Alf code. The proposed approach translates fUML activity diagrams and associated Alf code into equivalent Java code and then automatically generates: (1) input data needed to cover or execute all paths in the executable fUML and Alf models and (2) test cases and test oracle (expected output) for testing the actual implementation of the system under development. We also present a tool chain and demonstrate our proposed approach with the help of an example.

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Section: Code Generation From Adsmentioning

confidence: 99%

Exhaustive Simulation and Test Generation Using fUML Activity Diagrams

Iqbal

Ashraf

Truşcan

et al. 2019

Advanced Information Systems Engineering

View full text Add to dashboard Cite

show abstract

“…Embora atualmente a UML seja a linguagem padrão de fato para modelagem de software [8], trata-se de uma linguagem de modelagem de propósito geral, ou general purpose modeling language (GPML), nem sempre adequada para tipos de aplicação específicos. Por outro lado, linguagens de S. R. M. Canovas and C. E. Cugnasca Extending a Metamodel for Adaptive Programs:…”

Section: Introductionunclassified

Extending a Metamodel for Adaptive Programs: Specifying Adaptive Functions

Canovas

Cugnasca

2016

IEEE Latin Am. Trans.

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Model Driven Engineering (MDE) is a software development approach based on the use of models as essential artifacts. In the ideal scenario, source code is automatically generated from models by steps of transformations defined by mapping functions. A metamodel is a special type of model that describes the syntax of a modeling language. They are important because mapping functions reference their elements to define transformations, allowing the application of MDE. This paper proposes an extension of a metamodel of a domain specific modeling language for adaptive programs. This extension includes metaclasses, properties and constraints for specifying adaptive functions. An existing mapping function, which generates partial source code in BADAL language, was then updated to consider the new metamodel elements, becoming able to generate code for adaptive functions from the model specification. The resulting metamodel and mapping function were used as input to generate a CASE tool for adaptive programs, allowing the use of MDE in a higher level of abstraction than before for this class of application.

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Effects of variability in models: a family of experiments

et al. 2022

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The ever-growing need for customization creates a need to maintain software systems in many different variants. To avoid having to maintain different copies of the same model, developers of modeling languages and tools have recently started to provide implementation techniques for such variant-rich systems, notably variability mechanisms, which support implementing the differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of which have dedicated benefits and drawbacks. Currently, language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this article, we present an empirical assessment of annotative and compositional variability mechanisms for three popular types of models. We report and discuss findings from a family of three experiments with 164 participants in total, in which we studied the impact of different variability mechanisms during model comprehension tasks. We experimented with three model types commonly found in modeling languages: class diagrams, state machine diagrams, and activity diagrams. We find that, in two out of three experiments, annotative technique lead to better developer performance. Use of the compositional mechanism correlated with impaired performance. For all three considered tasks, the annotative mechanism was preferred over the compositional one in all experiments. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models.

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Code Generation for UML 2 Activity Diagrams

Cited by 5 publications

References 7 publications

Exhaustive Simulation and Test Generation Using fUML Activity Diagrams

Exhaustive Simulation and Test Generation Using fUML Activity Diagrams

Extending a Metamodel for Adaptive Programs: Specifying Adaptive Functions

Effects of variability in models: a family of experiments

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