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Auflistung nach Autor:in "Nassar, Nebras"

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  • Konferenzbeitrag
    Deducing model metrics from meta models
    (Modellierung 2016, 2016) Nassar, Nebras; Arendt, Thorsten; Taentzer, Gabriele
    The use of model-based software development has become more and more popular because it aims to increase the quality of software development. Therefore, the number and the size of model instances are cumulatively growing and software quality and quality assurance consequently lead back to the quality and quality assurance of the involved models. For model quality assurance, several quality aspects can be checked by the use of dedicated metrics. However, when using a domain specific modeling language, the manual creation of metrics for each specific domain is a repetitive and tedious process. In this paper, we present an approach to derive basic model metrics for any given modeling language by defining metric patterns typed by the corresponding meta-meta model. We discuss several concrete patterns and present an Eclipse-based tool which automates the process of basic model metrics derivation, generation, and calculation.
  • ConferencePaper
    MoFuzz: A Fuzzer Suite for Testing Model-Driven Software Engineering Tools
    (Software Engineering 2021, 2021) Nguyen, Hoang Lam; Nassar, Nebras; Kehrer, Timo; Grunske, Lars
    Fuzzing or fuzz testing is an established technique that aims to discover unexpected program behavior (\eg, bugs, vulnerabilities, or crashes) by feeding automatically generated data into a program under test. However, the application of fuzzing to test Model-Driven Software Engineering (MDSE) tools is still limited because of the difficulty of existing fuzzers to provide structured, well-typed inputs, namely models that conform to typing and consistency constraints induced by a given meta-model and underlying modeling framework. We present three different approaches for fuzzing MDSE tools: A graph grammar-based fuzzer and two variants of a coverage-guided mutation-based fuzzer working with different sets of model mutation operators. Our evaluation on a set of real-world MDSE tools shows that our approaches can outperform both standard fuzzers and model generators w.r.t. their fuzzing capabilities. Moreover, we found that each of our approaches comes with its own strengths and weaknesses in terms of code coverage and fault finding capabilities, thus complementing each other, forming a fuzzer suite for testing MDSE tools.