research-article

ReMoS: reducing defect inheritance in transfer learning via <u>re</u>levant <u>mo</u>del <u>sl</u>icing

Authors:

Yunxin LiuAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

Pages 1856 - 1868

https://doi.org/10.1145/3510003.3510191

Published: 05 July 2022 Publication History

Abstract

Transfer learning is a popular software reuse technique in the deep learning community that enables developers to build custom models (students) based on sophisticated pretrained models (teachers). However, like vulnerability inheritance in traditional software reuse, some defects in the teacher model may also be inherited by students, such as well-known adversarial vulnerabilities and backdoors. Reducing such defects is challenging since the student is unaware of how the teacher is trained and/or attacked. In this paper, we propose ReMoS, a relevant model slicing technique to reduce defect inheritance during transfer learning while retaining useful knowledge from the teacher model. Specifically, ReMoS computes a model slice (a subset of model weights) that is relevant to the student task based on the neuron coverage information obtained by profiling the teacher model on the student task. Only the relevant slice is used to finetune the student model, while the irrelevant weights are retrained from scratch to minimize the risk of inheriting defects. Our experiments on seven DNN defects, four DNN models, and eight datasets demonstrate that ReMoS can reduce inherited defects effectively (by 63% to 86% for CV tasks and by 40% to 61% for NLP tasks) and efficiently with minimal sacrifice of accuracy (3% on average).

References

[1]

[n. d.]. BERT Explained - A list of Frequently Asked Questions. https://huggingface.co/transformers/pretrained_models.html.

[2]

[n. d.]. Keras Applications. https://keras.io/api/applications/.

[3]

[n. d.]. Pytorch Hub. https://pytorch.org/hub/.

[4]

[n. d.]. Tensorflow Hub. https://www.tensorflow.org/hub.

[5]

Hiralal Agrawal, Joseph Robert Horgan, Edward W. Krauser, and Saul London. 1993. Incremental Regression Testing. In Proceedings of the Conference on Software Maintenance, ICSM 1993, Montréal, Quebec, Canada, September 1993, David N. Card (Ed.). IEEE Computer Society, 348--357.

[6]

Michael Backes, Sven Bugiel, and Erik Derr. 2016. Reliable third-party library detection in android and its security applications. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. 356--367.

Digital Library

[7]

Teodora Baluta, Zheng Leong Chua, Kuldeep S Meel, and Prateek Saxena. 2020. Scalable quantitative verification for deep neural networks. arXiv preprint arXiv:2002.06864 (2020).

[8]

Ravi Bhoraskar, Seungyeop Han, Jinseong Jeon, Tanzirul Azim, Shuo Chen, Jaeyeon Jung, Suman Nath, Rui Wang, and David Wetherall. 2014. Brahmastra: Driving apps to test the security of third-party components. In 23rd USENIX Security Symposium (USENIX Security 14). 1021--1036.

[9]

David Binkley. 1998. The application of program slicing to regression testing. Information and software technology 40, 11--12 (1998), 583--594.

[10]

Kai Chen, Xueqiang Wang, Yi Chen, Peng Wang, Yeonjoon Lee, XiaoFeng Wang, Bin Ma, Aohui Wang, Yingjun Zhang, and Wei Zou. 2016. Following devil's footprints: Cross-platform analysis of potentially harmful libraries on android and ios. In 2016 IEEE Symposium on Security and Privacy (SP). IEEE, 357--376.

[11]

Ting-Wu Chin, Cha Zhang, and Diana Marculescu. 2021. Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 3243--3252.

[12]

Todor Davchev, Timos Korres, Stathi Fotiadis, Nick Antonopoulos, and Subramanian Ramamoorthy. 2019. An Empirical Evaluation of Adversarial Robustness under Transfer Learning. CoRR abs/1905.02675 (2019).

[13]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[14]

Zakir Durumeric, Frank Li, James Kasten, Johanna Amann, Jethro Beekman, Mathias Payer, Nicolas Weaver, David Adrian, Vern Paxson, Michael Bailey, et al. 2014. The matter of heartbleed. In Proceedings of the 2014 conference on internet measurement conference. 475--488.

Digital Library

[15]

Yang Feng, Qingkai Shi, Xinyu Gao, Jun Wan, Chunrong Fang, and Zhenyu Chen. 2020. DeepGini: prioritizing massive tests to enhance the robustness of deep neural networks. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. 177--188.

Digital Library

[16]

William B Frakes and Kyo Kang. 2005. Software reuse research: Status and future. IEEE transactions on Software Engineering 31, 7 (2005), 529--536.

Digital Library

[17]

Xiang Gao, Ripon K. Saha, Mukul R. Prasad, and Abhik Roychoudhury. 2020. Fuzz testing based data augmentation to improve robustness of deep neural networks. In ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June - 19 July, 2020, Gregg Rothermel and Doo-Hwan Bae (Eds.). ACM, 1147--1158.

Digital Library

[18]

Antonios Gkortzis, Daniel Feitosa, and Diomidis Spinellis. 2019. A Double-Edged Sword? Software Reuse and Potential Security Vulnerabilities. In International Conference on Software and Systems Reuse. Springer, 187--203.

[19]

Martin L Griss. 1998. Software reuse: architecture, process and organization for business success. IEEE.

[20]

Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. 2017. Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733 (2017).

[21]

Jianmin Guo, Yu Jiang, Yue Zhao, Quan Chen, and Jiaguang Sun. 2018. DL-Fuzz: differential fuzzing testing of deep learning systems. In Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2018, Lake Buena Vista, FL, USA, November 04--09, 2018, Gary T. Leavens, Alessandro Garcia, and Corina S. Pasareanu (Eds.). ACM, 739--743.

Digital Library

[22]

Tibor Gyimóthy, Árpád Beszédes, and István Forgács. 1999. An Efficient Relevant Slicing Method for Debugging. In Software Engineering - ESEC/FSE'99, 7th European Software Engineering Conference, Held Jointly with the 7th ACM SIGSOFT Symposium on the Foundations of Software Engineering, Toulouse, France, September 1999, Proceedings (Lecture Notes in Computer Science), Oscar Nierstrasz and Michel Lemoine (Eds.), Vol. 1687. Springer, 303--321.

[23]

Song Han, Huizi Mao, and William J. Dally. 2016. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2--4, 2016, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1510.00149

[24]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.

[25]

Ling Huang, Anthony D Joseph, Blaine Nelson, Benjamin IP Rubinstein, and J Doug Tygar. 2011. Adversarial machine learning. In Proceedings of the 4th ACM workshop on Security and artificial intelligence. 43--58.

Digital Library

[26]

Yujin Huang, Han Hu, and Chunyang Chen. 2021. Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps. arXiv preprint arXiv:2101.04401 (2021).

[27]

Dennis Jeffrey and Neelam Gupta. 2008. Experiments with test case prioritization using relevant slices. J. Syst. Softw. 81, 2 (2008), 196--221.

[28]

Yujie Ji, Xinyang Zhang, Shouling Ji, Xiapu Luo, and Ting Wang. 2018. ModelReuse Attacks on Deep Learning Systems. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS 2018, Toronto, ON, Canada, October 15--19, 2018, David Lie, Mohammad Mannan, Michael Backes, and XiaoFeng Wang (Eds.). ACM, 349--363.

Digital Library

[29]

Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020. Is bert really robust? a strong baseline for natural language attack on text classification and entailment. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 8018--8025.

[30]

Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Li Fei-Fei. 2011. Novel Dataset for Fine-Grained Image Categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO.

[31]

Volodymyr Kuleshov, Shantanu Thakoor, Tingfung Lau, and Stefano Ermon. 2018. Adversarial examples for natural language classification problems. (2018).

[32]

Keita Kurita, Paul Michel, and Graham Neubig. 2020. Weight Poisoning Attacks on Pretrained Models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2793--2806.

[33]

Seokhyun Lee, Sooyoung Cha, Dain Lee, and Hakjoo Oh. 2020. Effective white-box testing of deep neural networks with adaptive neuron-selection strategy. In ISSTA '20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, USA, July 18--22, 2020, Sarfraz Khurshid and Corina S. Pasareanu (Eds.). ACM, 165--176.

Digital Library

[34]

Xingjian Li, Haoyi Xiong, Hanchao Wang, Yuxuan Rao, Liping Liu, and Jun Huan. 2019. Delta: Deep Learning Transfer using Feature Map with Attention for Convolutional Networks. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net. https://openreview.net/forum?id=rkgbwsAcYm

[35]

Yingwei Li, Song Bai, Cihang Xie, Zhenyu Liao, Xiaohui Shen, and Alan Yuille. 2019. Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses. arXiv preprint arXiv:1904.00979 (2019).

[36]

Yingwei Li, Song Bai, Yuyin Zhou, Cihang Xie, Zhishuai Zhang, and Alan Yuille. 2020. Learning Transferable Adversarial Examples via Ghost Networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34.

[37]

Yuanchun Li, Jiayi Hua, Haoyu Wang, Chunyang Chen, and Yunxin Liu. 2021. DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection. arXiv preprint arXiv:2101.06896 (2021).

[38]

Yuanchun Li, Ziqi Zhang, Bingyan Liu, Ziyue Yang, and Yunxin Liu. 2021. ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse Detection. Proceeding of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (2021).

Digital Library

[39]

Junyu Lin, Lei Xu, Yingqi Liu, and Xiangyu Zhang. 2020. Composite Backdoor Attack for Deep Neural Network by Mixing Existing Benign Features. In CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, USA, November 9--13, 2020, Jay Ligatti, Xinming Ou, Jonathan Katz, and Giovanni Vigna (Eds.). ACM, 113--131.

Digital Library

[40]

Bingyan Liu, Yifeng Cai, Yao Guo, and Xiangqun Chen. 2021. TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning. Proceedings of the 35th AAAI Conference on Artificial Intelligence (2021).

[41]

Bingyan Liu, Yao Guo, and Xiangqun Chen. 2019. WealthAdapt: A general network adaptation framework for small data tasks. In Proceedings of the 27th ACM International Conference on Multimedia. 2179--2187.

Digital Library

[42]

Bingyan Liu, Yuanchun Li, Yunxin Liu, Yao Guo, and Xiangqun Chen. 2020. Pmc: A privacy-preserving deep learning model customization framework for edge computing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 4 (2020), 1--25.

Digital Library

[43]

Changliu Liu, Tomer Arnon, Christopher Lazarus, Clark Barrett, and Mykel J Kochenderfer. 2019. Algorithms for verifying deep neural networks. arXiv preprint arXiv:1903.06758 (2019).

[44]

Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2018. Fine-pruning: Defending against backdooring attacks on deep neural networks. In International Symposium on Research in Attacks, Intrusions, and Defenses. Springer, 273--294.

[45]

Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang. 2018. Trojaning Attack on Neural Networks. In 25th Annual Network and Distributed System Security Symposium (NDSS). 18--221.

[46]

Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019).

[47]

Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning Word Vectors for Sentiment Analysis. In The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19--24 June, 2011, Portland, Oregon, USA, Dekang Lin, Yuji Matsumoto, and Rada Mihalcea (Eds.). The Association for Computer Linguistics, 142--150. https://aclanthology.org/P11-1015/

Digital Library

[48]

Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018. Towards Deep Learning Models Resistant to Adversarial Attacks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=rJzIBfZAb

[49]

John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020. TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 119--126.

[50]

Maria-Elena Nilsback and Andrew Zisserman. 2008. Automated Flower Classification over a Large Number of Classes. In Indian Conference on Computer Vision, Graphics and Image Processing.

Digital Library

[51]

Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2009), 1345--1359.

Digital Library

[52]

Ivan Pashchenko, Henrik Plate, Serena Elisa Ponta, Antonino Sabetta, and Fabio Massacci. 2018. Vulnerable open source dependencies: Counting those that matter. In Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. 1--10.

Digital Library

[53]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019), 8026--8037.

[54]

Brandon Paulsen, Jingbo Wang, and Chao Wang. 2020. ReluDiff: differential verification of deep neural networks. In ICSE '20: 42nd International Conference on Software Engineering, Seoul, South Korea, 27 June - 19 July, 2020, Gregg Rothermel and Doo-Hwan Bae (Eds.). ACM, 714--726.

Digital Library

[55]

Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. 2017. Deepxplore: Automated whitebox testing of deep learning systems. In proceedings of the 26th Symposium on Operating Systems Principles. 1--18.

Digital Library

[56]

Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018. Hypothesis Only Baselines in Natural Language Inference. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, ^*SEM@NAACL-HLT 2018, New Orleans, Louisiana, USA, June 5--6, 2018, Malvina Nissim, Jonathan Berant, and Alessandro Lenci (Eds.). Association for Computational Linguistics, 180--191.

[57]

Ariadna Quattoni and Antonio Torralba. 2009. Recognizing indoor scenes. In 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 413--420.

[58]

Shahbaz Rezaei and Xin Liu. 2020. A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https://openreview.net/forum?id=BylVcTNtDS

[59]

Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David W. Jacobs, and Tom Goldstein. 2020. Adversarially robust transfer learning. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https://openreview.net/forum?id=ryebG04YvB

[60]

David Shriver, Sebastian Elbaum, and Matthew B. Dwyer. 2021. Reducing DNN Properties to Enable Falsification with Adversarial Attacks. (2021).

[61]

Shoaib Ahmed Siddiqui, Andreas Dengel, and Sheraz Ahmed. 2020. Benchmarking Adversarial Attacks and Defenses for Time-Series Data. In Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23--27, 2020, Proceedings, Part III (Lecture Notes in Computer Science), Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, and Irwin King (Eds.), Vol. 12534. Springer, 544--554.

Digital Library

[62]

Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing. 1631--1642.

[63]

Yongqiang Tian, Zhihua Zeng, Ming Wen, Yepang Liu, Tzu-yang Kuo, and Shing-Chi Cheung. 2020. EvalDNN: A toolbox for evaluating deep neural network models. In 2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE, 45--48.

Digital Library

[64]

Bolun Wang, Yuanshun Yao, Bimal Viswanath, Haitao Zheng, and Ben Y. Zhao. 2018. With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning. In USENIX Security Symposium. USENIX Association, 1281--1297.

[65]

Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, and Peng Cheng. 2021. RobOT: Robustness-Oriented Testing for Deep Learning Systems. arXiv preprint arXiv:2102.05913 (2021).

[66]

Shuo Wang, Surya Nepal, Carsten Rudolph, Marthie Grobler, Shangyu Chen, and Tianle Chen. 2020. Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models. arXiv preprint arXiv:2001.03274 (2020).

[67]

P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona. 2010. Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001. California Institute of Technology.

[68]

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38--45. https://www.aclweb.org/anthology/2020.emnlp-demos.6

[69]

Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and Li Fei-Fei. 2011. Human action recognition by learning bases of action attributes and parts. In 2011 International conference on computer vision. IEEE, 1331--1338.

Digital Library

[70]

Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao. 2019. Latent Backdoor Attacks on Deep Neural Networks. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, CCS 2019, London, UK, November 11--15, 2019, Lorenzo Cavallaro, Johannes Kinder, XiaoFeng Wang, and Jonathan Katz (Eds.). ACM, 2041--2055.

Digital Library

[71]

Fuyuan Zhang, Sankalan Pal Chowdhury, and Maria Christakis. 2020. Deepsearch: A simple and effective blackbox attack for deep neural networks. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 800--812.

Digital Library

[72]

Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, and Meng Sun. 2020. Towards characterizing adversarial defects of deep learning software from the lens of uncertainty. arXiv preprint arXiv:2004.11573 (2020).

[73]

Ziqi Zhang, Yuanchun Li, Yao Guo, Xiangqun Chen, and Yunxin Liu. 2020. Dynamic slicing for deep neural networks. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 838--850.

Digital Library

Cited By

Zhai NZhou XLi SShi H(2025)Auxiliary Knowledge-Based Fine-Tuning Mechanism for Industrial Time-Lag Parameter PredictionIEEE Transactions on Automation Science and Engineering10.1109/TASE.2024.336232222(1220-1232)Online publication date: 2025
https://doi.org/10.1109/TASE.2024.3362322
De Martino VPalomba F(2025)Classification and challenges of non-functional requirements in ML-enabled systems: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2025.107678181(107678)Online publication date: May-2025
https://doi.org/10.1016/j.infsof.2025.107678
Ma MLi YChen YChen LZhou Y(2025)Why and How We Combine Multiple Deep Learning Models With Functional OverlapsJournal of Software: Evolution and Process10.1002/smr.7000337:2Online publication date: 16-Feb-2025
https://doi.org/10.1002/smr.70003
Show More Cited By

Index Terms

ReMoS: reducing defect inheritance in transfer learning via relevant model slicing

Recommendations

Dynamic slicing for deep neural networks
ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constructed with instructions and variables, rather than neural networks that ...
Lightening the Load with Highly Accurate Storage- and Energy-Efficient LightNNs
Special Issue on Deep learning on FPGAs

Hardware implementations of deep neural networks (DNNs) have been adopted in many systems because of their higher classification speed. However, while they may be characterized by better accuracy, larger DNNs require significant energy and area, thereby ...
On decomposing a deep neural network into modules
ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Deep learning is being incorporated in many modern software systems. Deep learning approaches train a deep neural network (DNN) model using training examples, and then use the DNN model for prediction. While the structure of a DNN model as layers is ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
,
Andreas Zeller
CISPA, Germany

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

In-Cooperation

IEEE CS

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Author Tags

Qualifiers

Research-article

Conference

ICSE '22

Sponsor:

SIGSOFT

ICSE '22: 44th International Conference on Software Engineering

May 21 - 29, 2022

Pennsylvania, Pittsburgh

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

19
Total Citations
View Citations
349
Total Downloads

Downloads (Last 12 months)61
Downloads (Last 6 weeks)5

Reflects downloads up to 27 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhai NZhou XLi SShi H(2025)Auxiliary Knowledge-Based Fine-Tuning Mechanism for Industrial Time-Lag Parameter PredictionIEEE Transactions on Automation Science and Engineering10.1109/TASE.2024.336232222(1220-1232)Online publication date: 2025
https://doi.org/10.1109/TASE.2024.3362322
De Martino VPalomba F(2025)Classification and challenges of non-functional requirements in ML-enabled systems: A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2025.107678181(107678)Online publication date: May-2025
https://doi.org/10.1016/j.infsof.2025.107678
Ma MLi YChen YChen LZhou Y(2025)Why and How We Combine Multiple Deep Learning Models With Functional OverlapsJournal of Software: Evolution and Process10.1002/smr.7000337:2Online publication date: 16-Feb-2025
https://doi.org/10.1002/smr.70003
Atoum IOtoom A(2024)Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point PredictionElectronics10.3390/electronics1323484313:23(4843)Online publication date: 8-Dec-2024
https://doi.org/10.3390/electronics13234843
Zhu KWang JZhou JWang ZChen HWang YYang LYe WZhang YGong NXie XLi BXu WChen JZhang YXue JWang SBai GYuan X(2024)PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial PromptsProceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis10.1145/3689217.3690621(57-68)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3689217.3690621
Bi XZhao RQi BSun HGao XYu YLiang Xd'Amorim M(2024)ModelFoundry: A Tool for DNN Modularization and On-Demand Model Reuse Inspired by the Wisdom of Software EngineeringCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663810(617-621)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3663810
Jiang WYasmin JJones JSynovic NKuo JBielanski NTian YThiruvathukal GDavis JSpinellis DConstantinou EBacchelli A(2024)PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source SoftwareProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644907(431-443)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644907
Zhu JWang LHan XLiu AXie T(2024)Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression Against Heterogeneous Attacks Toward AI Software DeploymentIEEE Transactions on Software Engineering10.1109/TSE.2023.334851550:3(376-390)Online publication date: Mar-2024
https://doi.org/10.1109/TSE.2023.3348515
Yao HLi ZHuang KLou JQin ZRen K(2024)RemovalNet: DNN Fingerprint Removal AttacksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.331506421:4(2645-2658)Online publication date: Jul-2024
https://doi.org/10.1109/TDSC.2023.3315064
Zhang ZGong CCai YYuan YLiu BLi DGuo YChen X(2024)No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device ML2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00052(3327-3345)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00052
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten