Classifying test suite effectiveness via model inference and ROBBDs

Hermann Felbinger*, Ingo Pill, Franz Wotawa

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deciding whether a given test suite is effective enough is certainly a challenging task. Focusing on a software program’s functionality, we propose in this paper a new method that leverages Boolean functions as abstract reasoning format. That is, we use machine learning in order to infer a special binary decision diagram from the considered test suite and extract a total variable order, if possible. Intuitively, if an ROBDD derived from the Boolean functions representing the program under test’s specification actually coincides with that of the test suite (using the same variable order), we conclude that the test suite is effective enough. That is, any program that passes such a test suite should clearly show the desired input-output behavior. In our paper, we provide the corresponding algorithms of our approach and their respective proofs. Our first experimental results illustrate our approach’s practicality and viability.

Original languageEnglish
Title of host publicationTests and Proofs - 10th International Conference, TAP 2016 Held as Part of STAF 2016, Proceedings
PublisherSpringer-Verlag Italia
Pages76-93
Number of pages18
Volume9762
ISBN (Print)9783319411347
DOIs
Publication statusPublished - 2016
Event10th International Conference on Tests and Proofs, TAP 2016 and Held as Part of Software Technologies: Applications and Foundations, STAF 2016 - Vienna, Austria
Duration: 5 Jul 20167 Jul 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9762
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference10th International Conference on Tests and Proofs, TAP 2016 and Held as Part of Software Technologies: Applications and Foundations, STAF 2016
CountryAustria
CityVienna
Period5/07/167/07/16

Keywords

  • BDD
  • Machine learning
  • ROBDD
  • Software testing

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Felbinger, H., Pill, I., & Wotawa, F. (2016). Classifying test suite effectiveness via model inference and ROBBDs. In Tests and Proofs - 10th International Conference, TAP 2016 Held as Part of STAF 2016, Proceedings (Vol. 9762, pp. 76-93). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9762). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-41135-4_5