MT-PART: Metamorphic-Testing-Based Adaptive Random Testing Through Partitioning

Zhihao Ying, Dave Towey, Tsong Yueh Chen, Zhi Quan Zhou

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

1 Citation (Scopus)

Abstract

Metamorphic Testing (MT) has been repeatedly proven effective in detecting software faults. MT detects faults by checking the Metamorphic Relations (MRs) among Source Test Cases (STCs) and Follow-up Test Cases (FTCs) and the corresponding outputs. Metamorphic Groups (MGs) denote the associated STCs and FTCs. The performance of MT relates strongly to the MRs and MGs. However, previous studies that on MG generation mainly focused on improving the effectiveness (i.e. fault-detection capability) of MT, but to some extent overlooked the efficiency. This paper proposes a new kind of MG generation algorithms called Metamorphic-Testing-based Adaptive Random Testing through Partitioning (MT-PART). These algorithms at-tempt to improve both the effectiveness and the efficiency of MT by dynamically partitioning the input domain and generating new STCs and FTCs that are uniformly distributed over their corresponding input domains. Through empirical experiments, we found that our algorithms are able to significantly outper-form other existing MG generation algorithms in terms of test efficiency, while maintaining good test effectiveness.
Original languageEnglish
Title of host publication2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)
PublisherIEEE
Pages1184-1193
Number of pages10
ISBN (Electronic)9798350376968
ISBN (Print)9798350376975
DOIs
Publication statusPublished - 2024

Keywords

  • metamorphic testing
  • metamorphic group
  • metamorphic relation
  • adaptive random testing
  • input domain

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