Using machine learning to classify test outcomes

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Roper, Richard; (2019) Using machine learning to classify test outcomes. In: 2019 IEEE International Conference On Artificial Intelligence Testing (AITest). IEEE, Piscataway, N.J., pp. 99-100. ISBN 978-1-7281-0492-8 (https://doi.org/10.1109/AITest.2019.00009)

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Abstract

When testing software it has been shown that there are substantial benefits to be gained from approaches which exercise unusual or unexplored interactions with a system - techniques such as random testing, fuzzing, and exploratory testing. However, such approaches have a drawback in that the outputs of the tests need to be manually checked for correctness, representing a significant burden for the software engineer. This paper presents a strategy to support the process of identifying which tests have passed or failed by combining clustering and semi-supervised learning. We have shown that by using machine learning it is possible to cluster test cases in such a way that those corresponding to failures concentrate into smaller clusters. Examining the test outcomes in cluster-size order has the effect of prioritising the results: those that are checked early on have a much higher probability of being a failing test. As the software engineer examines the results (and confirms or refutes the initial classification), this information is employed to bootstrap a secondary learner to further improve the accuracy of the classification of the (as yet) unchecked tests. Results from experimenting with a range of systems demonstrate the substantial benefits that can be gained from this strategy, and how remarkably accurate test output classifications can be derived from examining a relatively small proportion of results.

ORCID iDs

Roper, Richard ORCID logoORCID: https://orcid.org/0000-0001-6794-4637;
  • Item type:Book Section
    ID code:68454
    Dates:
    Date
    Event
    20 May 2019
    Published
    21 February 2019
    Accepted
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Computer and Information Sciences
    Strategic Research Themes > Health and Wellbeing
    Depositing user: Pure Administrator
    Date deposited:18 Jun 2019 11:40
    Last modified:19 Nov 2024 01:23
    URI:https://strathprints.strath.ac.uk/id/eprint/68454
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