DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems

Lei Ma, Felix Juefei-Xu, Minhui Xue, Bo Li, Li Li, Yang Liu, Jianjun Zhao

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

50 被引用数 (Scopus)

抄録

Deep learning (DL) has achieved remarkable progress over the past decade and has been widely applied to many industry domains. However, the robustness of DL systems recently becomes great concerns, where minor perturbation on the input might cause the DL malfunction. These robustness issues could potentially result in severe consequences when a DL system is deployed to safety-critical applications and hinder the real-world deployment of DL systems. Testing techniques enable the robustness evaluation and vulnerable issue detection of a DL system at an early stage. The main challenge of testing a DL system attributes to the high dimensionality of its inputs and large internal latent feature space, which makes testing each state almost impossible. For traditional software, combinatorial testing (CT) is an effective testing technique to balance the testing exploration effort and defect detection capabilities. In this paper, we perform an exploratory study of CT on DL systems. We propose a set of combinatorial testing criteria specialized for DL systems, as well as a CT coverage guided test generation technique. Our evaluation demonstrates that CT provides a promising avenue for testing DL systems.

本文言語英語
ホスト出版物のタイトルSANER 2019 - Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering
編集者Emad Shihab, David Lo, Xinyu Wang
出版社Institute of Electrical and Electronics Engineers Inc.
ページ614-618
ページ数5
ISBN(電子版)9781728105918
DOI
出版ステータス出版済み - 3 15 2019
イベント26th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2019 - Hangzhou, 中国
継続期間: 2 24 20192 27 2019

出版物シリーズ

名前SANER 2019 - Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering

会議

会議26th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2019
国/地域中国
CityHangzhou
Period2/24/192/27/19

All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • ソフトウェア
  • 計算理論と計算数学

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