3 research outputs found

    OCL-based Runtime Monitoring of JVM hosted Applications

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    In this paper we present an approach that enables users to monitor and verify the behavior of an application running on a virtual machine at the model level. Concrete implementations of object-oriented software usually contain a lot of technical classes. Thus, the central parts of an application, e.g., the business rules, may be hidden among peripheral functionality like user-interface classes or classes managing persistency. Our approach makes use of modern virtual machines and allows the devloper to profile an application in order to achieve an abstract monitoring and verification of central application components. We represent virtual machine bytecode in form of a so-called platform-aligned model (PAM) comprising OCL invariants and pre- and postconditions. In contrast to related work, our approach uses the original source or bytecode of the monitored application as it stands and does not require any changes. We show a prototype implementation as an extension of the UML and OCL tool USE. Also, we investigate the impact of our approach to the execution time of a monitored system

    Automating Java Program Testing Using OCL and AspectJ

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    Random testing can eliminate subjectiveness in constructing test data and increase the diversity of test data. However, one difficult problem is to construct test oracles that decide test results---test failures or successes. Assertions can be used as test oracles and are most effective when derived from formal specifications such as OCL constraints. If fully automated, random testing can reduce the cost of testing dramatically. In this paper we propose an approach for automating Java program testing by combining random testing and OCL. The key idea of our approach is to use OCL constraints as test oracles by translating them to runtime checks written in AspectJ. We realize our approach by adapting existing frameworks for translating OCL to AspectJ and assertion-based random testing. We evaluate the effectiveness of our approach through case studies and experiments. Our approach can detect errors in both implementations and OCL constraints and provide a practical means for using OCL in design and programming

    On Formalizing UML and OCL Features and Their Employment to Runtime Verification

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    Model-driven development (MDD) has been identified as a promising approach for developing software. By using abstract models of a system and by generating parts of the system out of these models, one tries to improve the efficiency of the overall development process and the quality of the resulting software. In the context of MDD the Unified Modeling Language (UML) and its related textual Object Constraint Language (OCL) have gained a high recognition. To be able to generate systems of high quality and to allow for interoperability between modeling tools, a well-defined semantics for these languages is required. This thesis summarizes published work in this context that employs an endogenous metamodeling approach to define the semantics of newer elements of the UML. While the covered elements are exhaustively used to define relations between elements of the metamodel of the UML, the UML specification leaves out a precise definition of their semantics. Our proposed approach uses models, not only to define the abstract syntax, but also to define the semantics of UML. By using UML and OCL for this, existing modeling tools can be used to validate the definition. The second part of this thesis covers work on the usage of UML and OCL models for runtime verification. It is shown how models can still be used at the end of a software development process, i. e., after an implementation has manually been added to generated parts, even though they are not used as central parts of the development process. This work also influenced the integration of protocol state machines into a modeling tool, which lead to publications about the runtime semantics of state machines and the capabilities to declaratively specify behavior using state machines
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