Biagiola, M., Cardozo, N., Shin, D. orcid.org/0000-0002-0840-6449 et al. (3 more authors) (2023) Summary of the fourth international workshop on deep learning for testing and testing for deep learning (DeepTest 2023). ACM SIGSOFT Software Engineering Notes, 48 (4). pp. 39-40. ISSN 0163-5948
Abstract
Deep Learning (DL) techniques help software developers thanks to their ability to learn from historical information which is useful in several program analysis and testing tasks (e.g., malware detection, fuzz testing, bug-finding, and type-checking). DL-based software systems are also increasingly adopted in safety-critical domains, such as autonomous driving, medical diagnosis, and aircraft collision avoidance systems. In particular, testing the correctness and reliability of DL-based systems is paramount, since a failure of such systems would cause a significant safety risk for the involved people and/or environment. The 4th International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023) was co-located with the 45th International Conference on Software Engineering (ICSE), with the goal of targeting research at the intersection of software engineering and deep learning and devise novel approaches and tools to ensure the interpretability and dependability of software systems that depends on DL components.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 Copyright is held by the owner/author(s). This is an author-produced version of a paper subsequently published in ACM SIGSOFT Software Engineering Notes. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Nov 2023 11:27 |
Last Modified: | 10 Nov 2023 11:27 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Refereed: | Yes |
Identification Number: | 10.1145/3617946.3617953 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205147 |