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.
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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 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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