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Fri 29 May 2020 11:50 - 12:10 at TBD1 - Deep Learning Testing and Debugging 3

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.

Fri 29 May

icse-2020-paper-presentations
10:50 - 12:30: Paper Presentations - Deep Learning Testing and Debugging 3 at TBD1
icse-2020-papers10:50 - 11:10
Talk
Nargiz HumbatovaUniversità della Svizzera italiana, Gunel JahangirovaUniversità della Svizzera italiana, Gabriele BavotaUniversità della Svizzera italiana, Vincenzo RiccioUniversità della Svizzera italiana, Andrea StoccoUniversità della Svizzera italiana, Paolo TonellaUniversità della Svizzera italiana
icse-2020-papers11:10 - 11:30
Talk
Yuchi TianColumbia University, Ziyuan ZhongColumbia University, Vicente OrdonezUniversity of Virginia, Gail KaiserColumbia University, New York, Baishakhi RayColumbia University, New York
icse-2020-papers11:30 - 11:50
Talk
Md Johirul IslamIowa State University, Rangeet PanIowa State University, Giang NguyenDept. of Computer Science, Iowa State University, Hridesh RajanIowa State University
icse-2020-papers11:50 - 12:10
Talk
Xiang GaoNational University of Singapore, Ripon SahaFujitsu Laboratories of America, Inc., Mukul PrasadFujitsu Laboratories of America, Inc, Abhik RoychoudhuryNational University of Singapore
icse-2020-papers12:10 - 12:30
Talk
Ru ZhangMicrosoft Research, Wencong XiaoAlibaba, Hongyu ZhangThe University of Newcastle, Yu LiuMicrosoft Research, Haoxiang LinMicrosoft Research, Mao YangMicrosoft Research