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A case study of the recursive least squares estimation approach to adaptive testing for software components

  • Hai Hu*
  • , W. Eric Wong
  • , Chang Hai Jiang
  • , Kai Yuan Cai
  • *Corresponding author for this work
  • University of Texas at Dallas
  • Beihang University

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

Abstract

The strategy used for testing a software system should not be fixed, because as time goes on we may have a better understanding of the software under test. A solution to this problem is to introduce control theory into software testing. We can use adaptive testing where the testing strategy is adjusted on-line by using the data collected during testing. Since the use of software components in software development is increasing, it is now more important than ever to adopt a good strategy for testing software components. In this paper, we use an adaptive testing strategy for testing software components. This strategy (AT_RLSEc with c indicating components) applies a recursive least squares estimation (RLSE) method to estimate parameters such as failure detection rate. It is different from the genetic algorithm-based adaptive testing (AT_GA) where a genetic algorithm is used for parameter estimation. Experimental data from our case study suggest that the fault detection effectiveness of AT_RLSEc is better than that of AT_GA and random testing.

Original languageEnglish
Title of host publicationProceedings - Fifth International Conference on Quality Software, QSIC 2005
Pages135-141
Number of pages7
DOIs
StatePublished - 2005
Event5th International Conference on Quality Software, QSIC 2005 - Melbourne, Australia
Duration: 19 Sep 200520 Sep 2005

Publication series

NameProceedings - International Conference on Quality Software
Volume2005
ISSN (Print)1550-6002

Conference

Conference5th International Conference on Quality Software, QSIC 2005
Country/TerritoryAustralia
CityMelbourne
Period19/09/0520/09/05

Keywords

  • Adaptive testing
  • Controlled Markov chain
  • Software cybernetics
  • Software testing

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