Concolic Testing for Deep Neural Networks
- Submitting institution
-
Queen's University of Belfast
- Unit of assessment
- 12 - Engineering
- Output identifier
- 179520749
- Type
- E - Conference contribution
- DOI
-
10.1145/3238147.3238172
- Title of conference / published proceedings
- Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering
- First page
- 109
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- September
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
C - Electrical and Electronic
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Deep learning is widely applied by industry, however, there are growing concerns regarding the emerging safety and security challenges implied by it. This paper delivers a significant trustworthy deep learning software development. It bridges the gap between advanced (concolic) testing and the quality assurance of deep learning software. The paper and its tool are collaborative output with our industry partner Dstl.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -