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ICSE 2020
Wed 24 June - Thu 16 July 2020
Thu 9 Jul 2020 00:12 - 00:24 at Goguryeo - P14-Testing Chair(s): Shin Yoo

Deep neural networks (DNN) have been shown to be notoriously brittle to small perturbations in their input data. This problem is analogous to the over-fitting problem in test-based program synthesis and automatic program repair, which is a consequence of the incomplete specification, the limited tests or training examples, that the program synthesis or repair algorithm has to learn from. Recently, test generation techniques have been successfully employed to augment existing specifications of intended program behavior, to improve the generalizability of program synthesis and repair. Inspired by these approaches, in this paper, we propose a technique that re-purposes software testing methods, specifically mutation-based fuzzing, to augment the training data of DNNs, with the objective of enhancing their robustness. Our technique casts the DNN data augmentation problem as an optimization problem. It uses genetic search to generate the most suitable variant of an input data to use for training the DNN, while simultaneously identifying opportunities to accelerate training by skipping augmentation in many instances. We instantiate this technique in two tools, SENSEI and SENSEI-SA, and evaluate them on 15 DNN models spanning 5 popular image data-sets. Our evaluation shows that SENSEI can improve the robust accuracy of the DNN, compared to the state of the art, on each of the 15 models, by upto 11.9% and 5.5% on average. Further, SENSEI-SA can reduce the average DNN training time by 25%, while still improving robust accuracy.

Thu 9 Jul
Times are displayed in time zone: (UTC) Coordinated Universal Time change

00:00 - 01:00: P14-TestingPaper Presentations / Technical Papers / Software Engineering in Practice at Goguryeo
Chair(s): Shin YooKorea Advanced Institute of Science and Technology
00:00 - 00:12
Seenomaly: Vision-Based Linting of GUI Animation Effects Against Design-Don’t GuidelinesTechnical
Technical Papers
Dehai ZhaoAustralian National University, Zhenchang XingAustralia National University, Chunyang ChenMonash University, Xiwei XuData 61, Liming ZhuCSIRO's Data61 and UNSW, Guoqiang LiShanghai Jiao Tong University, Jinshui WangSchool of Information Science and Engineering, Fujian University of Technology, Fuzhou, China
00:12 - 00:24
Fuzz Testing based Data Augmentation to Improve Robustness of Deep Neural NetworksTechnical
Technical Papers
Xiang GaoNational University of Singapore, Singapore, Ripon SahaFujitsu Laboratories of America, Inc., Mukul R. PrasadFujitsu Laboratories of America, Inc, Abhik RoychoudhuryNational University of Singapore, Singapore
00:24 - 00:36
Modeling and Ranking Flaky Tests at AppleSEIP
Software Engineering in Practice
Emily KowalczykApple Inc., Karan NairApple, Zebao GaoApple, Leopold SilbersteinApple Inc., Teng LongApple, Atif MemonApple Inc.
00:36 - 00:48
Testing File System Implementations on Layered ModelsTechnicalArtifact Available
Technical Papers
Dongjie ChenNanjing University, Yanyan JiangNanjing University, Chang XuNanjing University, Xiaoxing MaNanjing University, Jian LuNanjing University
00:48 - 01:00
A Cost-efficient Approach to Building in Continuous IntegrationTechnical
Technical Papers
Xianhao JinVirginia Tech, USA, Francisco ServantVirginia Tech