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Auflistung nach Autor:in "Leblebici, Erhan"

1 - 3 von 3
  • Konferenzbeitrag
    A Catalogue of Optimization Techniques for Triple Graph Grammars
    (Modellierung 2014, 2014) Leblebici, Erhan; Anjorin, Anthony; Schürr, Andy
    Bidirectional model transformation languages are typically declarative, being able to provide unidirectional operationalizations from a common specification automatically. Declarative languages have numerous advantages, but ensuring runtime efficiency, especially without any knowledge of the underlying transformation engine, is often quite challenging. Triple Graph Grammars (TGGs) are a prominent example for a completely declarative, bidirectional language and have been successfully used in various application scenarios. Although an optimization phase based on profiling results is often a necessity to meet runtime requirements, there currently exists no systematic classification and evaluation of optimization strategies for TGGs, i.e., the optimization process is typically an ad-hoc process. In this paper, we investigate the runtime scalability of an exemplary bidirectional model-to-text transformation. While systematically optimizing the implementation, we introduce, classify and apply a series of optimization strategies. We provide in each case a quantitative measurement and qualitative discussion, establishing a catalogue of current and future optimization techniques for TGGs in particular and declarative rule-based model transformation languages in general.
  • Konferenzbeitrag
    Model-driven Development of Virtual Network Embedding Algorithms with Model Transformation and Linear Optimization Techniques
    (Modellierung 2018, 2018) Tomaszek, Stefan; Leblebici, Erhan; Wang, Lin; Schürr, Andy
    Enhancing the scalability and utilization of data centers, virtualization is a promising technology to manage, develop and operate network functions in a flexible way. For the placement of virtual networks in the data center, many approaches and algorithms are discussed in the literature to optimize solving the so-called virtual network embedding problem with respect to various optimization goals. This paper presents a new approach for the model-driven specification, simulation-based evaluation, and implementation of possible mapping algorithms that respect a set of given constraints and using linear optimization solving techniques to select one almost optimal mapping. Rule-based model transformation techniques are used to translate a given mapping problem into a linear optimization problem by taking domain specific knowledge into account. The resulting framework thus supports the design and evaluation of (correct-by-construction) virtual network embedding algorithms on a high level of abstraction. Well-defined model transformation rule refinement strategies can be used to reduce the search space for the employed linear optimization techniques.
  • Konferenzbeitrag
    On Controlling the Attack Surface of Object-Oriented Refactorings
    (Software Engineering 2020, 2020) Ruland, Sebastian; Kulcsár, Géza; Leblebici, Erhan; Peldszus, Sven; Lochau, Malte
    The results of this work have originally been published in. Refactorings constitute an effective means to improve quality and maintainability of evolving object-oriented programs. Search-based techniques have shown promising results in finding near-optimal sequences of behavior-preserving program transformations that (1) maximize code-quality metrics and (2) minimize the number of code changes. However, the impact of refactorings on non-functional properties like security has received little attention so far. To this end, we propose, as a further objective, to minimize the attack surface of object-oriented programs (i.e., to maximize strictness of declared accessibility of class members). Minimizing the attack surface naturally competes with applicability of established refactorings like MoveMethod, frequently used for improving code quality in terms of coupling/cohesion measures. Our tool implementation is based on an EMF meta-model for Java-like programs and utilizes MOMoT, a search-based model-transformation and optimization framework. Our experimental results gained from applying different accessibility-control strategies to a collection of real-world Java programs show the impact of attack surface minimization on design-improving refactorings. We further compare the results to those of existing refactoring tools.