Summary of 'An empirical comparison of combinatorial testing and search-based testing in the context of automated and autonomous driving systems'

Florian Kluck*, Yihao Li, Jianbo Tao, Franz Wotawa

*Corresponding author for this work

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

Abstract

In automated and autonomous driving, we need to verify the behavior considering critical interactions with the environment. To find such interactions, we need an appropriate test suite generation methodology. This paper summarizes the findings we published last year in a journal comparing combinatorial, search-based, and random testing utilizing the same input model. We focused on detecting faults and combinatorial coverage, considering two automated emergency braking function implementations in our experimental evaluation.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2024
PublisherIEEE
Pages178-179
Number of pages2
ISBN (Electronic)9798350344790
DOIs
Publication statusPublished - 17 Sept 2024
Event2024 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2024 - Toronto, Canada
Duration: 27 May 202431 May 2024

Conference

Conference2024 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2024
Country/TerritoryCanada
CityToronto
Period27/05/2431/05/24

Keywords

  • Automated ADAS testing
  • automated testing for autonomous driving
  • combinatorial testing
  • comparing testing methodologies
  • random testing
  • search-based testing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Safety, Risk, Reliability and Quality
  • Modelling and Simulation

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