Fault detection effectiveness of spathic test data

J. H. Hayes, Pifu Zhang

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

1 Scopus citations

Abstract

This paper presents an approach for generating test data for unit-level, and possibly integration-level, testing based on sampling over intervals of the input probability distribution, i.e., one that has been divided or layered according to criteria. Our approach is termed "spathic" as it selects random values felt to be most likely or least likely to occur from a segmented input probability distribution. Also, it allows the layers to be further segmented if additional test data is required later in the test cycle. The spathic approach finds a middle ground between the more difficult to achieve adequacy criteria and random test data generation, and requires less effort on the part of the tester. It can be viewed as guided random testing, with the tester specifying some information about expected input. The spathic test data generation approach can be used to augment "intelligent" manual unit-level testing. An initial case study suggests that spathic test sets defect more faults than random test data sets, and achieve higher levels of statement and branch coverage.

Original languageEnglish
Title of host publicationProceedings - 8th IEEE international Conference on Engineering of Complex Computer Systems, ICECCS 2002
Pages183-192
Number of pages10
ISBN (Electronic)0769517579
DOIs
StatePublished - 2002
Event8th IEEE international Conference on Engineering of Complex Computer Systems, ICECCS 2002 - Greenbelt, United States
Duration: Dec 2 2002Dec 4 2002

Publication series

NameProceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS
Volume2002-January

Conference

Conference8th IEEE international Conference on Engineering of Complex Computer Systems, ICECCS 2002
Country/TerritoryUnited States
CityGreenbelt
Period12/2/0212/4/02

Keywords

  • Computer networks
  • Computer science
  • Data engineering
  • Distributed computing
  • Fault detection
  • Performance evaluation
  • Probability distribution
  • Reliability engineering
  • Sampling methods
  • System testing

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications

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