Candidate test set reduction for adaptive random testing: An overheads reduction technique

Rubing Huang, Haibo Chen, Weifeng Sun, Dave Towey

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)

Abstract

Adaptive Random Testing (ART) is a family of testing techniques that were proposed as an enhancement of random testing (RT). ART achieves better failure-detection capability than RT by more evenly distributing test cases throughout the input domain. However, this process of selecting more diverse test cases incurs a heavy computational cost. In this paper, we propose a new ART method that improves on the efficiency of Fixed-Size-Candidate-Set ART (FSCS) by applying a test set reduction strategy. The proposed method, FSCS by Candidate Test Set Reduction (FSCS-CTSR), reduces the number of randomly generated candidate test cases, but supplements them with earlier, unused candidates that have lower similarity to the executed test cases. Simulations and experimental studies were conducted to examine the effectiveness and efficiency of the method, with the experimental results showing a comparable failure-detection effectiveness to FSCS, but with lower computational costs.

Original languageEnglish
Article number102730
JournalScience of Computer Programming
Volume214
DOIs
Publication statusPublished - 1 Feb 2022

Keywords

  • Adaptive random testing
  • FSCS
  • Random testing
  • Software testing
  • Test set reduction

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'Candidate test set reduction for adaptive random testing: An overheads reduction technique'. Together they form a unique fingerprint.

Cite this