Poster: Is Euclidean Distance the best Distance Measurement for Adaptive Random Testing?

Rubing Huang, Chenhui Cui, Weifeng Sun, Dave Towey

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

5 Citations (Scopus)

Abstract

Adaptive random testing (ART) aims at enhancing the testing effectiveness of random testing (RT) by more evenly spreading test cases over the input domain. Many ART methods have been proposed, based on various, different notions. For example, distance-based ART (DART) makes use of the concept of distance to implement ART, attempting to generate new test cases that are far away from previously executed ones. The Euclidean distance has been a popular choice of distance metric, used in DART to evaluate the differences between test cases. However, is the Euclidean distance the most suitable choice for DART? To answer this question, we conducted a series of simulations to investigate the impact that the Euclidean distance, and its many variations, has on the testing effectiveness of DART. The results show that when the dimensionality of the input domain is low, the Euclidean distance may indeed be a good choice. However, when the dimensionality is high, it appears to be less suitable.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 13th International Conference on Software Testing, Verification and Validation, ICST 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages406-409
Number of pages4
ISBN (Electronic)9781728157771
DOIs
Publication statusPublished - Oct 2020
Event13th IEEE International Conference on Software Testing, Verification and Validation, ICST 2020 - Porto, Portugal
Duration: 23 Mar 202027 Mar 2020

Publication series

NameProceedings - 2020 IEEE 13th International Conference on Software Testing, Verification and Validation, ICST 2020

Conference

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

Keywords

  • Euclidean distance
  • adaptive random testing
  • dissimilarity metrics

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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