Testing stochastic software using Pseudo-oracles
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Patrick, M
Craig, AP
Cunniffe, NJ
Parry, M
Gilligan, CA
Abstract
Stochastic models can be difficult to test due to their complexity and randomness, yet their predictions are often used to make important decisions, so they need to be correct. We introduce a new search-based technique for testing implementations of stochastic models by maximising the differences between the implementation and a pseudo-oracle. Our technique reduces testing effort and enables discrepancies to be found that might otherwise be overlooked. We show the technique can identify differences challenging for humans to observe, and use it to help a new user understand implementation differences in a real model of a citrus disease (Huanglongbing) used to inform policy and research.
Description
Keywords
computational models, testing, search-based optimisation
Journal Title
ISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis
Conference Name
ISSTA '16: International Symposium on Software Testing and Analysis
Journal ISSN
Volume Title
Publisher
ACM
Publisher DOI
Sponsorship
Wellcome Trust