Summary of SWFC-ART: a cost-effective approach for fixed-size-candidate-set adaptive random testing through small world graphs

Muhammad Ashfaq, Rubing Huang, Dave Towey, Michael Omari, Dmitry Yashunin, Patrick Kwaku Kudjo, Tao Zhang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

This extended abstract presents an approach to enhance the Fixed-Sized-Candidate-Set Adaptive Random Testing (FSCS-ART) sampling strategy. SWFC-ART, the proposed approach, stores the previously-executed, non-failure-causing test cases into a Hierarchical Navigable Small World Graph (HNSWG) data structure and uses an efficient and consistent Nearest Neighbor Search (NNS) mechanism, especially for high-dimensional input domains. Our experiments show that SWFC-ART reduces the computational overhead of FSCS-ART from quadratic to log-linear order while retaining the failure-detection effectiveness of FSCS-ART.
Original languageEnglish
Title of host publication2022 IEEE Conference on Software Testing, Verification and Validation (ICST)
PublisherIEEE
Pages460-460
Number of pages1
ISBN (Electronic)9781665466790
DOIs
Publication statusPublished - 2022
Event15th IEEE International Conference on Software Testing, Verification and Validation - Virtual: Appendee
Duration: 4 Apr 202213 Apr 2022
https://icst2022.vrain.upv.es/

Conference

Conference15th IEEE International Conference on Software Testing, Verification and Validation
Abbreviated titleICST 2022
Period4/04/2213/04/22
Internet address

Keywords

  • Software Testing
  • Random Testing
  • Adaptive Random Testing
  • Efficiency
  • Hierarchical Navigable Small World Graphs

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