Auflistung nach Schlagwort "Modeling Tools"
1 - 3 von 3
Treffer pro Seite
Sortieroptionen
- KonferenzbeitragMessage from the Modellierung’22 Tools & Demos Chairs(Modellierung 2022 Satellite Events, 2022) Hacks, Simon; Bork, DominikModellierung 2022 has a dedicated track for the newest modeling tools. The aim of this track is to present modeling tools that have been and are being developed by the modeling community.
- ConferencePaperMoFuzz: A Fuzzer Suite for Testing Model-Driven Software Engineering Tools(Software Engineering 2021, 2021) Nguyen, Hoang Lam; Nassar, Nebras; Kehrer, Timo; Grunske, LarsFuzzing 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.
- ZeitschriftenartikelVerläßlichkeitsbewertung komplexer Systeme(Informatik-Spektrum: Vol. 21, No. 6, 1998) Thurner, Erwin M.; Dal Cin, Mario; Schneeweiß, Winfrid G.Die Akzeptanz von technischen Systemen wird wesentlich davon beeeinflußt, daß sie ihre spezifizierte Funktion verläßlich – d.h. sicher und zuverlässig – ausführen. Insbesondere bei komplexen Systemen sind daher eine strukturierte Vorgehensweise und die Verwendung von mathematisch fundierten Methoden unerläßlich zu ihrer Bewertung und zur gezielten Verbesserung. Der vorliegende Aufsatz gibt zunächst einen Überblick über die gebräuchlichsten Meßgrößen zur Ver-läßlichkeitsanalyse. Auf dieser Basis werden Bewertungsmethoden wie Fehlerbäume, Markov-Methoden und Stochastische Petri-Netze vorgestellt und ihre Grenzen diskutiert. Eine Auswahl von Modellierungs-Werk-zeugen gibt Einblick, welche Kriterien bei der Modellierung beachtet werden sollten. Abschließend werden Vorgehensweisen und Methoden anhand eines Beispiels aus der industriellen Praxis erläutert.Summary The acceptance of technical systems is essentially determined by their performing in a dependable way, i.e. safely and reliably. Particularly in complex systems structured approach and the use of mathematical methods are important for their evaluation and controled enhancement. This paper first gives a survey about the most common measures used in dependability analysis. Based on this, evaluation methods such as fault trees, Markov chains, and stochastic Petri nets are introduced and discussed with respect to their immanent limits. Furthermore, several modeling tools are shown to demonstrate some criteria for system modeling. Finally, modeling tasks and methods are explained by a real-world example.