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Exploiting augmented intelligence in the modeling of safety-critical autonomous systems

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Formal Aspects of Computing

Abstract

Machine learning (ML) is used increasingly in safety-critical systems to provide more complex autonomy to make the system to do decisions by itself in uncertain environments. Using ML to learn system features is fundamentally different from manually implementing them in conventional components written in source code. In this paper, we make a first step towards exploring the architecture modeling of safety-critical autonomous systems which are composed of conventional components and ML components, based on natural language requirements. Firstly, augmented intelligence for restricted natural language requirement modeling is proposed. In that, several AI technologies such as natural language processing and clustering are used to recommend candidate terms to the glossary, as well as machine learning is used to predict the category of requirements. The glossary including data dictionary and domain glossary and the category of requirements will be used in the restricted natural language requirement specification method RNLReq, which is equipped with a set of restriction rules and templates to structure and restrict the way how users document requirements. Secondly, automatic generation of SysML architecture models from the RNLReq requirement specifications is presented. Thirdly, the prototype tool is implemented based on Papyrus. Finally, it presents the evaluation of the proposed approach using an industrial autonomous guidance, navigation and control case study.

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References

  1. Aniculaesei A, Arnsberger D, Howar F, Rausch A (2016) Towards the verification of safety-critical autonomous systems in dynamic environments. In: Kargahi M, Trivedi A (eds) Proceedings of the the first workshop on verification and validation of cyber-physical systems, V2CPS@IFM 2016, Reykjavík, Iceland, June 4–5, 2016, volume 232 of EPTCS, pp 79–90

  2. Arora, C., Sabetzadeh, M., Briand, L.C., Zimmer, F.: Automated checking of conformance to requirements templates using natural language processing. IEEE Trans Softw Eng 41(10), 944–968 (2015)

    Article  Google Scholar 

  3. Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F.: Automated extraction and clustering of requirements glossary terms. IEEE Trans Softw Eng 43(10), 918–945 (2016)

    Article  Google Scholar 

  4. Arora C, Sabetzadeh M, Nejati S, Briand LC (2019) An active learning approach for improving the accuracy of automated domain model extraction. ACM Trans Softw Eng Methodol 28(1):4:1–4:34

  5. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and regression trees. CRC Press (1984)

    MATH  Google Scholar 

  6. Cohen, W.W., Ravikumar, P., Fienberg, S.E., et al.: A comparison of string distance metrics for name-matching tasks. IIWeb 2003, 73–78 (2003)

    Google Scholar 

  7. David, H.A.M., Sifakis, J., (July, : Autonomics: In search of a foundation for next-generation autonomous systems. PNAS 117(30), 17491–17498 (2020)

  8. Dario, I.M.M., Pan, B.: A survey on artificial intelligence trends in spacecraft guidance dynamics and control. Astrodyn 3, 287–299 (2019)

    Article  Google Scholar 

  9. Space Segment Operability (ECSS-E-ST-70-11C). Research report, European Cooperation for Space Standardization (ECSS), 2008

  10. EASA Artificial Intelligence Roadmap 1.0. A human-centric approach to AI in aviation. Research report, EASA, February 2020

  11. Ferrell TK, Ferrell UD (2017) RTCA DO-178C/EUROCAE ED-12C. Digital Avionics Handbook

  12. Feiler PH, Gluch DP (2013) Model-based engineering with AADL: An introduction to the SAE architecture analysis & design language. Pearson Schweiz Ag

  13. Fraser, D., Giaquinta, R., Hoffmann, R., Ireland, M., Miller, A., Norman, G.: Collaborative models for autonomous systems controller synthesis. Formal Aspects Comput. 32(2), 157–186 (2020)

    Article  MathSciNet  Google Scholar 

  14. Gomaa, W.H., Fahmy, A.A., et al.: A survey of text similarity approaches. Int J Comput Appl 68(13), 13–18 (2013)

    Google Scholar 

  15. He H 2014) Hanlp: Han language processing. https://github.com/hankcs/HanLP

  16. Harmain, H.M., Gaizauskas, R.: Cm-builder: A natural language-based case tool for object-oriented analysis. Autom. Softw. Eng. 10(2), 157–181 (2003)

    Article  Google Scholar 

  17. Ben AW, Herchi H (2012) From user requirements to uml class diagram. arXiv preprint

  18. Horkoff J (2019) Non-functional requirements for machine learning: Challenges and new directions. In: 27th IEEE International Requirements Engineering Conference, RE 2019, Jeju Island, Korea (South), September 23–27, 2019. IEEE, pp 386–391

  19. Haq FU, Shin D, Nejati S, Briand LC (2020) Comparing offline and online testing of deep neural networks: An autonomous car case study. In: 13th IEEE international conference on software testing, validation and verification, ICST 2020, Porto, Portugal, October 24-28, 2020. IEEE, pp 85–95

  20. Samad A, Bajwa IS, Shahzad M (2009) Object oriented software modeling using nlp based knowledge extraction. Eur J Sci Res

  21. Ishikawa F, Yoshioka N (2019) How do engineers perceive difficulties in engineering of machine-learning systems?: questionnaire survey. In: Proceedings of the joint 7th international workshop on conducting empirical studies in industry and 6th international workshop on software engineering research and industrial practice, CESSER-IP@ICSE 2019, Montreal, QC, Canada, May 27, 2019. IEEE / ACM, pp 2–9

  22. Meß J-G, Dannemann F, Fabian G (2019) Techniques of artificial intelligence for space applications: A survey. In: European workshop on on-board data processing (OBDP2019)

  23. Jiule, T., Wei, Z.: Method for calculating similarity of words based on cilin. J Jilin Univ (Inf Sci) 6, 602–608 (2010)

    Google Scholar 

  24. Katz, G., Huang, D.A., Ibeling, D., Julian, K., Lazarus, C., Lim, R., Shah, P., Thakoor, S., Wu, H., Zeljic, A., Dill, D.L., Kochenderfer, M.J., Barrett, C.W.: The marabou framework for verification and analysis of deep neural networks. In: Dillig, I., Tasiran, S. (eds.) Computer aided verification-31st international conference, CAV 2019, New York City, NY, USA, July 15–18, 2019, Proceedings, Part I. lecture notes in computer science, vol. 11561, pp. 443–452. Springer (2019)

    Google Scholar 

  25. Kothari V, Liberis E, Lane ND (2020) The final frontier: Deep learning in space. In: Pillai P, Lv Q (eds) HotMobile '20: The 21st International Workshop on Mobile Computing Systems and Applications, Austin, TX, USA, March 3-4, 2020. ACM, pp 45–49

  26. Kirwan, R.F., Miller, A., Porr, B.: Model checking learning agent systems using promela with embedded C code and abstraction. Formal Aspects Comput 28(6), 1027–1056 (2016)

    Article  MathSciNet  Google Scholar 

  27. Liu C, Arnon T, Lazarus C, Barrett CW, Kochenderfer MJ (2019) Algorithms for verifying deep neural networks. CoRR, abs/1903.06758

  28. Liu L, Feng L, Cao Z, Li J (2016) Requirements engineering for health data analytics: Challenges and possible directions. In: 24th IEEE International Requirements Engineering Conference, RE 2016, Beijing, China, September 12–16, 2016. IEEE Computer Society, pp 266–275

  29. Liu, Q.: Word similarity computing based on hownet. Computational linguistics and Chinese language processing 7(2), 59–76 (2002)

    Google Scholar 

  30. Madni AM (2020) Exploiting augmented intelligence in systems engineering and engineered systems. Insight, pp 31–36

  31. Manning, C., Raghavan, P., Schütze, H.: Introduction to information retrieval. Nat Lang Eng 16(1), 100–103 (2010)

    Article  Google Scholar 

  32. Mavin A, Wilkinson P, Harwood A, Novak M (2009) Easy approach to requirements syntax (ears). In: Requirements engineering conference, pp 277–282

  33. Nascimento AM, Vismari LF, Molina C BST, Cugnasca PS, Batista CJJr, de Almeida JRJr, Inam R, Fersman E, Marquezini MV, Hata AY (2019) A systematic literature review about the impact of artificial intelligence on autonomous vehicle safety. CoRR, abs/1904.02697

  34. Perini, A., Susi, A., Avesani, P.: A machine learning approach to software requirements prioritization. IEEE Trans Softw Eng 39(4), 445–461 (2013)

    Article  Google Scholar 

  35. Sifakis, J.: Autonomous systems - an architectural characterization. In: Boreale, M., Corradini, F., Loreti, M., Pugliese, R. (eds.) Models, languages, and tools for concurrent and distributed programming - essays dedicated to Rocco De Nicola on the occasion of his 65th birthday. Lecture notes in computer science, vol. 11665, pp. 388–410. Springer (2019)

    Chapter  Google Scholar 

  36. Sood S, Loguinov D (2011) Probabilistic near-duplicate detection using simhash. In: Proceedings of the 20th ACM international conference on information and knowledge management, pp 1117–1126

  37. Stottler D, Ramachandran S, Belardi C, Mandayam R (2020) On-board, autonomous, hybrid spacecraft subsystem fault and anomaly detection, diagnosis, and recovery. In: Advanced Maui optical and Space Surveillance Technologies Conference (AMOS)

  38. Tipaldi, M., Glielmo, L.: A survey on model-based mission planning and execution for autonomous spacecraft. IEEE Syst J 12(4), 3893–3905 (2018)

    Article  Google Scholar 

  39. Vogelsang A, Borg M (2019) Requirements engineering for machine learning: Perspectives from data scientists. In: 27th IEEE international requirements engineering conference workshops, RE 2019 workshops, Jeju Island, Korea (South), September 23-27, 2019. IEEE, pp 245–251

  40. Wahiba AAS, Azzouz ZB, Dey N (2016) Automatic builder of class diagram (abcd): an application of uml generation from functional requirements. Software Pract Exp

  41. Wang, Z., Li, J., Zhao, Y., Qi, Y., Pu, G., He, J., Gu, B.: Spardl: A requirement modeling language for periodic control system. In: Margaria, T., Steffen, B. (eds.) Leveraging applications of formal methods, verification, and vaidation, pp. 594–608. Springer, Berlin Heidelberg, Berlin, Heidelberg (2010)

    Chapter  Google Scholar 

  42. Winkler J, Vogelsang A (2016) Automatic classification of requirements based on convolutional neural networks. In: 24th IEEE international requirements engineering conference, RE 2016, Beijing, China, September 12–16, 2016. IEEE Computer Society, pp 39–45

  43. Wang, F., Yang, Z., Huang, Z., Liu, C., Zhou, Y., Bodeveix, J.P., Filali, M.: An approach to generate the traceability between restricted natural language requirements and AADL models. IEEE Trans Reliab 69(1), 154–173 (2020)

    Article  Google Scholar 

  44. Yue T, Briand LC, Labiche Y (2013) Facilitating the transition from use case models to analysis models: Approach and experiments. ACM Trans Softw Eng Methodol 22(1):5:1–5:38

  45. Yue T, Briand LC, Labiche Y (2015) atoucan: An automated framework to derive UML analysis models from use case models. ACM Trans Softw Eng Methodol 24(3):13:1–13:52

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Acknowledgements

Supported by the National Natural Science Foundation of China (62072233), Aviation Science Fund of China (201919052002), and the Fundamental Research Funds for the Central Universities (NP2017205).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhibin Yang, Yang Bao, Yongqiang Yang & Zhiqiu Huang

  2. IRIT-University of Toulouse, Toulouse, France

    Jean-Paul Bodeveix & Mamoun Filali

  3. Department of Applied Physics and Electronics, Umea University, Umeå, Sweden

    Zonghua Gu

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  1. Zhibin Yang
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  2. Yang Bao
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Correspondence to Zhibin Yang.

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Zhiming Liu, Xiaoping Chen, Ji Wang and Jim Woodcock

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Yang, Z., Bao, Y., Yang, Y. et al. Exploiting augmented intelligence in the modeling of safety-critical autonomous systems. Form Asp Comp 33, 343–384 (2021). https://doi.org/10.1007/s00165-021-00543-6

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