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Inferring Automatic Test Oracles

Langdon, WB; Yoo, S; Harman, M; (2017) Inferring Automatic Test Oracles. In: Proceedings of the 2017 IEEE/ACM 10th International Workshop on Search-Based Software Testing (SBST). (pp. pp. 5-6). IEEE: Buenos Aires, Argentina. Green open access

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Abstract

We propose the use of search based learning from existing open source test suites to automatically generate partially correct test oracles. We argue that mutation testing and nversion computing (augmented by deep learning and other soft computing techniques), will be able to predict whether a program’s output is correct sufficiently accurately to be useful.

Type: Proceedings paper
Title: Inferring Automatic Test Oracles
Event: 2017 IEEE/ACM 10th International Workshop on Search-Based Software Testing (SBST)
Location: Buenos Aires, ARGENTINA
Dates: 22 May 2017 - 23 May 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/SBST.2017.1
Publisher version: http://dx.doi.org/10.1109/SBST.2017.1
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: SBSE, Multiplicity computing, deep testing, Search Based Automatic Oracles, SOFTWARE
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10055925
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