Well-informed Test Case Generation and Crash Reproduction

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Search-based test data generation approaches have come a long way over the past few years, but these approaches still have some limitations when it comes to exercising specific behavior for triggering particular kinds of faults (e.g., crashes or specific types of integration between classes/modules). In this thesis, we are investigating new fitness functions and evolutionary-based algorithms and techniques to tackle these limitations. We have defined multiple novel approaches for crash reproduction and class integration testing. Currently, we are still working on improving both crash reproduction and class integration testing.

Original languageEnglish
Title of host publication2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST)
Subtitle of host publicationProceedings
EditorsE. O'Conner
Place of PublicationPiscataway
PublisherIEEE
Pages424-426
Number of pages3
ISBN (Electronic)978-1-7281-5778-8
ISBN (Print)978-1-7281-5779-5
DOIs
Publication statusPublished - 2020
Event13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020 - Porto, Portugal
Duration: 23 Mar 202027 Mar 2020

Conference

Conference13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020
CountryPortugal
CityPorto
Period23/03/2027/03/20

Keywords

  • automated crash reproduction
  • automated integration testing
  • search-based software testing

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  • Cite this

    Derakhshanfar, P. (2020). Well-informed Test Case Generation and Crash Reproduction. In E. O'Conner (Ed.), 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST): Proceedings (pp. 424-426). [9159102] IEEE. https://doi.org/10.1109/ICST46399.2020.00054