Volume

Dagstuhl Seminar Proceedings, Volume 8351

Publication Details

  • published at: 2009-05-25
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

Access Numbers

Documents

No documents found matching your filter selection.
Document
DOI: 10.4230/DagSemProc.08351.1
08351 Abstracts Collection – Evolutionary Test Generation

Authors: Holger Schlingloff, Tanja E. J. Vos, and Joachim Wegener

Abstract
From September 24th to September 29th 2008 the Dagstuhl Seminar 08351 ``Evolutionary Test Generation '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
Cite as

Holger Schlingloff, Tanja E. J. Vos, and Joachim Wegener. 08351 Abstracts Collection – Evolutionary Test Generation. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{schlingloff_et_al:DagSemProc.08351.1,
  author =	{Schlingloff, Holger and Vos, Tanja E. J. and Wegener, Joachim},
  title =	{{08351 Abstracts Collection – Evolutionary Test Generation}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.1},
  URN =		{urn:nbn:de:0030-drops-20231},
  doi =		{10.4230/DagSemProc.08351.1},
  annote =	{Keywords: Software-testing, evolutionary algoritms, meta-heuristic search}
}
Document
DOI: 10.4230/DagSemProc.08351.2
08351 Summary – Evolutionary Test Generation

Authors: Holger Schlingloff, Tanja E. J. Vos, and Joachim Wegener

Abstract
From September 24th to September 29th 2008 the Dagstuhl Seminar 08351 ``Evolutionary Test Generation '' was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. This paper contains an executive summary of the seminar and the open problems that were found.
Cite as

Holger Schlingloff, Tanja E. J. Vos, and Joachim Wegener. 08351 Summary – Evolutionary Test Generation. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{schlingloff_et_al:DagSemProc.08351.2,
  author =	{Schlingloff, Holger and Vos, Tanja E. J. and Wegener, Joachim},
  title =	{{08351 Summary – Evolutionary Test Generation}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.2},
  URN =		{urn:nbn:de:0030-drops-20224},
  doi =		{10.4230/DagSemProc.08351.2},
  annote =	{Keywords: Software-testing, evolutionary algoritms, meta-heuristic search}
}
Document
DOI: 10.4230/DagSemProc.08351.3
Co-testability Transformation

Authors: Philip McMinn

Abstract
This paper introduces the notion of ‘co-testability transformation’. As opposed to traditional testability transformations, which replace the original program in testing, co-testability transformations are designed to be used in conjunction with the original program (and any additional co-transformations as well). Until now, testability transformations have only been used to improve test data generation. However, co-testability transformations can function as partial oracles. This paper demonstrates practical usage of a co-testability transformation for automatically detecting floating-point errors in program code.
Cite as

Philip McMinn. Co-testability Transformation. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{mcminn:DagSemProc.08351.3,
  author =	{McMinn, Philip},
  title =	{{Co-testability Transformation}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--1},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.3},
  URN =		{urn:nbn:de:0030-drops-20131},
  doi =		{10.4230/DagSemProc.08351.3},
  annote =	{Keywords: Search-based testing, testability transformation}
}
Document
DOI: 10.4230/DagSemProc.08351.4
Fuzzy Logic Based Objective Function Construction for Evolutionary Test Generation

Authors: Andrea G. B. Tettamanzi

Abstract
The test case generation problem can be stated as an optimization problem whereby the closeness of test cases to violating the postcondition of a formal specification is maximized, subject to satisfying its precondition. This is usually implemented by constructing an objective function which provides a real-valued estimate of how distant all of the constraints are from being violated, and then trying to minimize it. A problem with this approach is that such objective functions may contain plateaux, which make their minimization hard. We propose a similar approach, grounded on fuzzy logic, which uses, instead of a "distance from violation" objective function, a fuzzy degree of proximity to postcondition violation and produces plateaux-free objective functions by construction. The approach is illustrated with the help of a case study on the functional (black-box) testing of computer programs.
Cite as

Andrea G. B. Tettamanzi. Fuzzy Logic Based Objective Function Construction for Evolutionary Test Generation. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{tettamanzi:DagSemProc.08351.4,
  author =	{Tettamanzi, Andrea G. B.},
  title =	{{Fuzzy Logic Based Objective Function Construction for Evolutionary Test Generation}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.4},
  URN =		{urn:nbn:de:0030-drops-20165},
  doi =		{10.4230/DagSemProc.08351.4},
  annote =	{Keywords: Functional testing, fuzzy logic, objective function}
}
Document
DOI: 10.4230/DagSemProc.08351.5
Ideas on Signal Generation for Evolutionary Testing of Continuous Systems

Authors: Andreas Windisch

Abstract
Test case generation constitutes a critical activity in software testing that is cost-intensive, time-consuming and error-prone when done manually. Hence, an automation of this process is required. One automation approach is search-based testing for which the task of generating test data is transformed into an optimization problem which is solved using metaheuristic search techniques. However, only little work has so far been done to apply search-based testing techniques to systems that depend on continuous input signals rather than single discrete input values. This paper proposes three novel approaches to generating input signals from within search-based testing techniques for continuous systems.
Cite as

Andreas Windisch. Ideas on Signal Generation for Evolutionary Testing of Continuous Systems. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{windisch:DagSemProc.08351.5,
  author =	{Windisch, Andreas},
  title =	{{Ideas on Signal Generation for Evolutionary Testing of Continuous Systems}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.5},
  URN =		{urn:nbn:de:0030-drops-20118},
  doi =		{10.4230/DagSemProc.08351.5},
  annote =	{Keywords: Search-Based Testing, Optimization, Metaheuristic}
}
Document
DOI: 10.4230/DagSemProc.08351.6
SAT-based Automatic Test Pattern Generation

Authors: Rolf Drechsler, Stephan Eggersglüß, Görschwin Fey, and Daniel Tille

Abstract
Due to the rapidly growing size of integrated circuits, there is a need for new algorithms for Automatic Test Pattern Generation (ATPG). While classical algorithms reach their limit, there have been recent advances in algorithms to solve Boolean Satisfiability (SAT). Because Boolean SAT solvers are working on Conjunctive Normal Forms (CNF), the problem has to be transformed. During transformation, relevant information about the problem might get lost and therefore is not available in the solving process. In the following we briefly motivate the problem and provide the latest developments in the field. The technique was implemented and experimental results are presented. The approach was combined with the ATPG framework of NXP Semiconductors. Significant improvements in overall performance and robustness are demonstrated.
Cite as

Rolf Drechsler, Stephan Eggersglüß, Görschwin Fey, and Daniel Tille. SAT-based Automatic Test Pattern Generation. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{drechsler_et_al:DagSemProc.08351.6,
  author =	{Drechsler, Rolf and Eggersgl\"{u}{\ss}, Stephan and Fey, G\"{o}rschwin and Tille, Daniel},
  title =	{{SAT-based Automatic Test Pattern Generation}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.6},
  URN =		{urn:nbn:de:0030-drops-20152},
  doi =		{10.4230/DagSemProc.08351.6},
  annote =	{Keywords: Circuit, ATPG, SAT, Boolean Satisfiability}
}
Document
DOI: 10.4230/DagSemProc.08351.7
SoftwareTesting with Active Learning in a Graph

Authors: Nicolas Baskiotis, Michèle Sebag, and Marie-Claude Gaudel

Abstract
Motivated by Structural Statistical Software Testing (SSST), this paper is interested in sampling the feasible execution paths in the control flow graph of the program being tested. For some complex programs, the fraction of feasible paths becomes tiny, ranging in $[10^{-10}, 10^{-5}]$. When relying on the uniform sampling of the program paths, SSST is thus hindered by the non-Markovian nature of the ``feasible path'' concept, due to the long-range dependencies between the program nodes. A divide and generate approach relying on an extended Parikh Map representation is proposed to address this limitation; experimental validation on real-world and artificial problems demonstrates gains of orders of magnitude compared to the state of the art.
Cite as

Nicolas Baskiotis, Michèle Sebag, and Marie-Claude Gaudel. SoftwareTesting with Active Learning in a Graph. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{baskiotis_et_al:DagSemProc.08351.7,
  author =	{Baskiotis, Nicolas and Sebag, Mich\`{e}le and Gaudel, Marie-Claude},
  title =	{{SoftwareTesting with Active Learning in a Graph}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.7},
  URN =		{urn:nbn:de:0030-drops-20149},
  doi =		{10.4230/DagSemProc.08351.7},
  annote =	{Keywords: Structural Statistical Software Testing, Active Learning, Control Flow Graph, Feaisble Paths, Parikh maps.}
}
Document
DOI: 10.4230/DagSemProc.08351.8
Using evolutionary algorithms to select parameters from equivalence classes

Authors: Felix Lindlar and Abel Marrero Pérez

Abstract
This paper presents some ideas about an approach which aims at extending existing methodologies for functional testing. Experience in automotive applications has shown that when selecting parameters for functional testing, many times a tester has equivalence classes in mind. Instead of losing valuable information in the process, support should be given to make them manageable. The proposed approach suggests evolutionary testing strategies to search for critical representatives within equivalence classes.
Cite as

Felix Lindlar and Abel Marrero Pérez. Using evolutionary algorithms to select parameters from equivalence classes. In Evolutionary Test Generation. Dagstuhl Seminar Proceedings, Volume 8351, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

Copy BibTex To Clipboard

@InProceedings{lindlar_et_al:DagSemProc.08351.8,
  author =	{Lindlar, Felix and Marrero P\'{e}rez, Abel},
  title =	{{Using evolutionary algorithms to select parameters from equivalence classes}},
  booktitle =	{Evolutionary Test Generation},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8351},
  editor =	{Holger Schlingloff and Tanja E. J. Vos and Joachim Wegener},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08351.8},
  URN =		{urn:nbn:de:0030-drops-20120},
  doi =		{10.4230/DagSemProc.08351.8},
  annote =	{Keywords: Equivalence classes, evolutionary testing, functional testing, automotive industry}
}

Filters

  • Authors
  • B
  • Baskiotis, Nicolas
  • D
  • Drechsler, Rolf
  • E
  • Eggersglüß, Stephan
  • F
  • Fey, Görschwin
  • G
  • Gaudel, Marie-Claude
  • L
  • Lindlar, Felix
  • M
  • Marrero Pérez, Abel
  • McMinn, Philip
  • S
  • Schlingloff, Holger
  • Sebag, Michèle
  • T
  • Tettamanzi, Andrea G. B.
  • Tille, Daniel
  • V
  • Vos, Tanja E. J.
  • W
  • Wegener, Joachim
  • Windisch, Andreas

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact