A comparison of three black-box optimization approaches to model-based testing

Teemu Kanstrén, Marsha Chechik

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

Model-based testing is a technique for generating test cases from a test model. Various notations and techniques have been used to express the test model and generate test cases from those models. Many use customized modelling languages and in-depth white-box static analysis for test generation. This allows for optimizing generated tests to specific paths in the model. Others use general-purpose programming languages and light-weight black-box dynamic analysis. While this light-weight approach allows for quick prototyping and easier integration with existing tools and user skills, optimizing the resulting test suite becomes more challenging since less information about the possible paths is available. In this paper, we present and compare three approaches to such black-box optimization.
Original languageEnglish
Title of host publicationProceedings of the 2014 Federated Conference on Computer Science and Information Systems
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages1591-1598
Volume2
ISBN (Print)978-83-60810-58-3
DOIs
Publication statusPublished - 2014
MoE publication typeA4 Article in a conference publication
EventFederated Conference on Computer Science and Information Systems, FedCSIS - Warsaw, Poland
Duration: 7 Sep 201410 Sep 2014

Conference

ConferenceFederated Conference on Computer Science and Information Systems, FedCSIS
Abbreviated titleFedCSIS
CountryPoland
CityWarsaw
Period7/09/1410/09/14

Keywords

  • algorithm design and analysis
  • analytical models
  • generators
  • greedy algorithms
  • optimization
  • radiation detectors
  • testing

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