Using model-based diagnosis to improve software testing

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

13 Scopus citations

Abstract

We propose a combination of AI techniques to improve software testing. When a test fails, a model-based diagnosis (MBD) algorithm is used to propose a set of possible explanations. We call these explanations diagnoses. Then, a planning algorithm is used to suggest further tests to identify the correct diagnosis. A tester preforms these tests and reports their outcome back to the MBD algorithm, which uses this information to prune incorrect diagnoses. This iterative process continues until the correct diagnosis is returned. We call this testing paradigm Test, Diagnose and Plan (TDP). Several test planning algorithms are proposed to minimize the number of TDP iterations, and consequently the number of tests required until the correct diagnosis is found. Experimental results show the benefits of using an MDP-based planning algorithms over greedy test planning in three benchmarks.

Original languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages1135-1141
Number of pages7
ISBN (Electronic)9781577356783
StatePublished - 1 Jan 2014
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: 27 Jul 201431 Jul 2014

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Conference

Conference28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
Country/TerritoryCanada
CityQuebec City
Period27/07/1431/07/14

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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