skip to main content
research-article

A Novel GAPG Approach to Automatic Property Generation for Formal Verification: The GAN Perspective

Published: 05 January 2023 Publication History

Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected Version of Record was published on March 14, 2023. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this citation page.

Abstract

Formal methods have been widely used to support software testing to guarantee correctness and reliability. For example, model checking technology attempts to ensure that the verification property of a specific formal model is satisfactory for discovering bugs or abnormal behavior from the perspective of temporal logic. However, because automatic approaches are lacking, a software developer/tester must manually specify verification properties. A generative adversarial network (GAN) learns features from input training data and outputs new data with similar or coincident features. GANs have been successfully used in the image processing and text processing fields and achieved interesting and automatic results. Inspired by the power of GANs, in this article, we propose a GAN-based automatic property generation (GAPG) approach to generate verification properties supporting model checking. First, the verification properties in the form of computational tree logic (CTL) are encoded and used as input to the GAN. Second, we introduce regular expressions as grammar rules to check the correctness of the generated properties. These rules work to detect and filter meaningless properties that occur because the GAN learning process is uncontrollable and may generate unsuitable properties in real applications. Third, the learning network is further trained by using labeled information associated with the input properties. These are intended to guide the training process to generate additional new properties, particularly those that map to corresponding formal models. Finally, a series of comprehensive experiments demonstrate that the proposed GAPG method can obtain new verification properties from two aspects: (1) using only CTL formulas and (2) using CTL formulas combined with Kripke structures.

Supplementary Material

3517154-vor (3517154-vor.pdf)
Version of Record for “A Novel GAPG Approach to Automatic Property Generation for Formal Verification: The GAN Perspective” by Gao et al., ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, No. 1 (TOMM 19:1).

References

[1]
Christian Bird, Venkatesh-Prasad Ranganath, Thomas Zimmermann, Nachiappan Nagappan, and Andreas Zeller. 2014. Extrinsic influence factors in software reliability: A study of 200,000 windows machines. In Proceedings of the 36th International Conference on Software Engineering. 205–214.
[2]
S. Reid. 2013. Software and Systems Engineering Software Testing Part 1: Concepts and Definitions. Technical Report. ISO/IEC/IEEE 29119-1.
[3]
Smruti Priyambada Nanda and Emanuel S. Grant. 2019. A survey of formal specification application to safety critical systems. In Proceedings of the IEEE 2nd International Conference on Information and Computer Technologies (ICICT). IEEE, 296–302.
[4]
Kazi Sakib, Zahir Tari, and Péter Bertók. 2013. Verification of Communication Protocols in Web Services: Model-checking Service Compositions. John Wiley & Sons.
[5]
Shiying He, Liansheng Huang, Ge Gao, Guanghong Wang, Zejing Wang, and Xiaojiao Chen. 2019. Design of real-time control in poloidal field power supply based on finite-state machine. IEEE Trans. Plasma Sci. 47, 4 (2019), 1878–1883.
[6]
IEEE Computer Society. 2019. IEEE standard for the functional verification language e. IEEE Std 1647-2019 (Revision of IEEE Std 1647-2016) (2019), 1–622. DOI:
[7]
Adeel Akram, Nannan Wang, Xinbo Gao, and Jie Li. 2018. Integrating GAN with CNN for face sketch synthesis. In Proceedings of the IEEE 4th International Conference on Computer and Communications (ICCC). IEEE, 1483–1487.
[8]
Xiang Gao, Yingjie Tian, and Zhiquan Qi. 2020. RPD-GAN: Learning to draw realistic paintings with generative adversarial network. IEEE Trans. Image Process. 29 (2020), 8706–8720.
[9]
Yang Yang, Xiaodong Dan, Xuesong Qiu, and Zhipeng Gao. 2020. FGGAN: Feature-guiding generative adversarial networks for text generation. IEEE Access 8 (2020), 105217–105225.
[10]
Honghao Gao, Danqi Chu, Yucong Duan, and Yuyu Yin. 2017. Probabilistic model checking-based service selection method for business process modeling. Int. J. Softw. Eng. Knowl. Eng. 27, 06 (2017), 897–923.
[11]
Vittoria Nardone, Antonella Santone, Massimo Tipaldi, Davide Liuzza, and Luigi Glielmo. 2018. Model checking techniques applied to satellite operational mode management. IEEE Syst. J. 13, 1 (2018), 1018–1029.
[12]
Honghao Gao, Huaikou Miao, Lilan Liu, Jinyu Kai, and Kun Zhao. 2018. Automated quantitative verification for service-based system design: A visualization transform tool perspective. Int. J. Softw. Eng. Knowl. Eng. 28, 10 (2018), 1369–1397.
[13]
Saoussen Mili, Nga Nguyen, and Rachid Chelouah. 2019. Transformation-based approach to security verification for cyber-physical systems. IEEE Syst. J. 13, 4 (2019), 3989–4000.
[14]
Honghao Gao, Huaikou Miao, and Hongwei Zeng. 2013. Predictive web service monitoring using probabilistic model checking. Appl. Math. Inf. Sci. 7, 1L (2013), 139–148.
[15]
Jin Cui, Zhenhua Duan, Cong Tian, and Hongwei Du. 2018. A novel approach to modeling and verifying real-time systems for high reliability. IEEE Trans. Reliab. 67, 2 (2018), 481–493.
[16]
Li Li, Jun Sun, Yang Liu, Meng Sun, and Jin-Song Dong. 2017. A formal specification and verification framework for timed security protocols. IEEE Trans. Softw. Eng. 44, 8 (2017), 725–746.
[17]
Zhao Lv, Shuming Chen, Tingrong Zhang, and Yaohua Wang. 2019. A specification-based semi-formal functional verification method by a stage transition graph model. IEEE Access 7 (2019), 14947–14958.
[18]
Sidra Sultana and Fahim Arif. 2017. Computational conversion via translation rules for transforming C++ code into UPPAAL’s automata. IEEE Access 5 (2017), 14455–14467.
[19]
Ansgar Rössig and Milena Petkovic. 2021. Advances in verification of ReLU neural networks. J. Global Optim. 81, 1 (2021), 109–152.
[20]
Mahum Naseer, Mishal Fatima Minhas, Faiq Khalid, Muhammad Abdullah Hanif, Osman Hasan, and Muhammad Shafique. 2020. FANNet: Formal analysis of noise tolerance, training bias and input sensitivity in neural networks. In Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 666–669.
[21]
Shanghui Yin, Renzhi Xing, Xiangqi Liu, Yinhui Yi, Kai Zheng, and Xin Huang. 2018. Model checking an artificial neural networks system in medical diagnosis. In Proceedings of the 9th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 852–856.
[22]
Andreas Venzke and Spyros Chatzivasileiadis. 2020. Verification of neural network behaviour: Formal guarantees for power system applications. IEEE Trans. Smart Grid 12, 1 (2020), 383–397.
[23]
Alessandro Abate, Daniele Ahmed, Mirco Giacobbe, and Andrea Peruffo. 2020. Formal synthesis of Lyapunov neural networks. IEEE Contr. Syst. Lett. 5, 3 (2020), 773–778.
[24]
Quoc-Sang Phan and Pasquale Malacaria. 2015. All-solution satisfiability modulo theories: Applications, algorithms and benchmarks. In Proceedings of the 10th International Conference on Availability, Reliability and Security. IEEE, 100–109.
[25]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014).
[26]
Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. (2014).
[27]
Tao Li, Xudong Liu, and Shihan Su. 2018. Semi-supervised text regression with conditional generative adversarial networks. In Proceedings of the IEEE International Conference on Big Data (Big Data). IEEE, 5375–5377.
[28]
Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, and Sarfraz Khurshid. 2018. DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems. In Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 132–142.
[29]
Sergio Segura, Gordon Fraser, Ana B. Sanchez, and Antonio Ruiz-Cortés. 2016. A survey on metamorphic testing. IEEE Trans. Softw. Eng. 42, 9 (2016), 805–824.
[30]
Ferenc Huszár. 2015. How (not) to train your generative model: Scheduled sampling, likelihood, adversary?arXiv preprint arXiv:1511.05101 (2015).
[31]
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. SeqGAN: Sequence generative adversarial nets with policy gradient. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2852–2858.
[32]
Yi-Lin Tuan and Hung-Yi Lee. 2019. Improving conditional sequence generative adversarial networks by stepwise evaluation. IEEE/ACM Trans. Audio, Speech Lang. Process. 27, 4 (2019), 788–798.
[33]
Umberto Rivieccio, Achim Jung, and Ramon Jansana. 2017. Four-valued modal logic: Kripke semantics and duality. J. Logic Computat. 27, 1 (2017), 155–199.
[34]
Christel Baier and Joost-Pieter Katoen. 2008. Principles of Model Checking. The MIT Press.
[35]
Yichen Qian, Jun Wu, Rui Wang, Fusheng Zhu, and Wei Zhang. 2019. Survey on reinforcement learning applications in communication networks. J. Commun. Inf. Netw. 4, 2 (2019), 30–39.
[36]
Tristan Cazenave. 2012. Monte Carlo beam search. IEEE Trans. Computat. Intell. AI Games 4, 1 (2012), 68–72.
[37]
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529, 7587 (2016), 484–489.
[38]
IEEE Power and Energy Society. 2018. IEEE standard for fall protection for electric utility transmission and distribution on poles and structures. IEEE Std 1307-2018 (Revision of IEEE Std 1307-2004) (2018), 1–46. DOI:
[39]
Kasem Khalil, Omar Eldash, Ashok Kumar, and Magdy Bayoumi. 2019. Economic LSTM approach for recurrent neural networks. IEEE Trans. Circ. Syst. II: Express Briefs 66, 11 (2019), 1885–1889.
[40]
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.
[41]
Yingqiong Peng, Muxin Liao, Yuxia Song, Zhichao Liu, Huojiao He, Hong Deng, and Yinglong Wang. 2019. FB-CNN: Feature fusion-based bilinear CNN for classification of fruit fly image. IEEE Access 8 (2019), 3987–3995.
[42]
Xinyu Lei, Hongguang Pan, and Xiangdong Huang. 2019. A dilated CNN model for image classification. IEEE Access 7 (2019), 124087–124095.

Cited By

View all
  • (2024)Illumination-Aware Low-Light Image Enhancement with Transformer and Auto-Knee CurveACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366465320:8(1-23)Online publication date: 29-Jun-2024
  • (2024)Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and ProspectACM Transactions on Design Automation of Electronic Systems10.1145/366130829:4(1-42)Online publication date: 21-Jun-2024
  • (2024)Real-Time Attentive Dilated U-Net for Extremely Dark Image EnhancementACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365466820:8(1-19)Online publication date: 12-Jun-2024
  • Show More Cited By

Index Terms

  1. A Novel GAPG Approach to Automatic Property Generation for Formal Verification: The GAN Perspective

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Multimedia Computing, Communications, and Applications
    ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 19, Issue 1
    January 2023
    505 pages
    ISSN:1551-6857
    EISSN:1551-6865
    DOI:10.1145/3572858
    • Editor:
    • Abdulmotaleb El Saddik
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 05 January 2023
    Online AM: 18 February 2022
    Accepted: 06 February 2022
    Revised: 17 January 2022
    Received: 12 October 2021
    Published in TOMM Volume 19, Issue 1

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Model checking
    2. verification property
    3. generative adversarial network (GAN)
    4. automatic property generation
    5. computational tree logic
    6. correctness and reliability

    Qualifiers

    • Research-article
    • Refereed

    Funding Sources

    • National Natural Science Foundation of China

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)260
    • Downloads (Last 6 weeks)16
    Reflects downloads up to 24 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Illumination-Aware Low-Light Image Enhancement with Transformer and Auto-Knee CurveACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366465320:8(1-23)Online publication date: 29-Jun-2024
    • (2024)Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and ProspectACM Transactions on Design Automation of Electronic Systems10.1145/366130829:4(1-42)Online publication date: 21-Jun-2024
    • (2024)Real-Time Attentive Dilated U-Net for Extremely Dark Image EnhancementACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365466820:8(1-19)Online publication date: 12-Jun-2024
    • (2024)cFedDT: Cross-Domain Federated Learning in Digital Twins for Metaverse Consumer Electronic ProductsIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332701070:1(3167-3182)Online publication date: Feb-2024
    • (2024)Meta-IDS: Meta-Learning-Based Smart Intrusion Detection System for Internet of Medical Things (IoMT) NetworkIEEE Internet of Things Journal10.1109/JIOT.2024.338729411:13(23080-23095)Online publication date: 1-Jul-2024
    • (2024)BPNN‐based flow classification and admission control for software defined IIoTIET Communications10.1049/cmu2.12798Online publication date: 5-Jul-2024
    • (2024)Steel product number recognition framework using semantic mask-conditioned diffusion model with limited dataJournal of Industrial Information Integration10.1016/j.jii.2024.10055938(100559)Online publication date: Mar-2024
    • (2024)Type classification and identification of IoT devices by using traffic characteristicsWireless Networks10.1007/s11276-024-03736-yOnline publication date: 23-Apr-2024
    • (2024)JMFEEL-Net: a joint multi-scale feature enhancement and lightweight transformer network for crowd countingKnowledge and Information Systems10.1007/s10115-023-02056-566:5(3033-3053)Online publication date: 30-Jan-2024
    • (2023)A novel Sybil attack detection scheme in mobile IoT based on collaborate edge computingEURASIP Journal on Wireless Communications and Networking10.1186/s13638-023-02233-82023:1Online publication date: 5-Mar-2023
    • Show More Cited By

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Full Text

    View this article in Full Text.

    Full Text

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media