Skip to main content
SpringerLink
Log in

Agility in Software 2.0 – Notebook Interfaces and MLOps with Buttresses and Rebars

  • Conference paper
  • First Online:
Lean and Agile Software Development (LASD 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 438))

Included in the following conference series:

Abstract

Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined “Software 2.0,” but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://bit.ly/3dKeUEH.

  2. 2.

    https://cowait.io.

  3. 3.

    https://jupyter.org.

  4. 4.

    https://databricks.com/product/collaborative-notebooks.

  5. 5.

    https://github.com/backtick-se/cowait.

  6. 6.

    https://cowait.io/docs/.

  7. 7.

    File System in Userspace, https://github.com/libfuse/libfuse.

  8. 8.

    https://kubernetes-csi.github.io/docs/.

References

  1. Giray, G.: A software engineering perspective on engineering machine learning systems: state of the art and challenges. J. Syst. Softw. 180, 111031 (2021)

    Article  Google Scholar 

  2. Ebert, C., Gallardo, G., Hernantes, J., Serrano, N.: DevOps. IEEE Softw. 33(3), 94–100 (2016)

    Article  Google Scholar 

  3. Diebold, P., Theobald, S.: How is agile development currently being used in regulated embedded domains? J. Softw. Evol. Process 30(8), e1935 (2018)

    Article  Google Scholar 

  4. Hanssen, G.K., Stålhane, T., Myklebust, T.: SafeScrum®-Agile Development of Safety-Critical Software. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99334-8

    Book  Google Scholar 

  5. Kim, M., Zimmermann, T., DeLine, R., Begel, A.: The emerging role of data scientists on software development teams. In: Proceedings of the 38th International Conference on Software Engineering, pp. 96–107 (2016)

    Google Scholar 

  6. Sletholt, M.T., Hannay, J.E., Pfahl, D., Langtangen, H.P.: What do we know about scientific software development’s agile practices? Comput. Sci. Eng. 14(2), 24–37 (2011)

    Article  Google Scholar 

  7. Sculley, D., et al.: Hidden technical debt in machine learning systems. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 2503–2511 (2015)

    Google Scholar 

  8. Amershi, S., et al.: Software engineering for machine learning: a case study. In: Proceedings of the 41st International Conference on Software Engineering, pp. 291–300 (2019)

    Google Scholar 

  9. Wan, Z., Xia, X., Lo, D., Murphy, G.C.: How does machine learning change software development practices? IEEE Trans. Software Eng. 47(9), 1857–1871 (2021)

    Google Scholar 

  10. Vogelsang, A., Borg, M.: Requirements engineering for machine learning: perspectives from data scientists. In: Proceedings of the 27th International Requirements Engineering Conference Workshops, pp. 245–251 (2019)

    Google Scholar 

  11. Chattopadhyay, S., Prasad, I., Henley, A.Z., Sarma, A., Barik, T.: What’s wrong with computational notebooks? Pain points, needs, and design opportunities. In: Human Factors in Computing Systems, pp. 1–12 (2020)

    Google Scholar 

  12. Jakobsson, M., Henriksson, J.: Sharing local files with Kubernetes clusters. MSc thesis, Lund University (2021). http://lup.lub.lu.se/student-papers/record/9066685/file/9066686.pdf

  13. Knuth, D.E.: Literate programming. Comput. J. 27(2), 97–111 (1984)

    Article  MATH  Google Scholar 

  14. Knuth, D.E.: Literate Programming. Center for the Study of Language and Information, Stanford, US (1992)

    Google Scholar 

  15. Hannay, J.E., MacLeod, C., Singer, J., Langtangen, H.P., Pfahl, D., Wilson, G.: How do scientists develop and use scientific software? In: Proceedings of the ICSE Workshop on Software Engineering for Computational Science and Engineering, pp. 1–8. IEEE (2009)

    Google Scholar 

  16. Vognstrup Fog, B., Nylandsted Klokmose, C.: Mapping the landscape of literate computing. In: Proceedings of the 30th Annual Workshop of the Psychology of Programming Interest Group (2019)

    Google Scholar 

  17. Kery, M.B., John, B.E., O’Flaherty, P., Horvath, A., Myers, B.A.: Towards effective foraging by data scientists to find past analysis choices. In: Human Factors in Computing Systems, pp. 1–13 (2019)

    Google Scholar 

  18. Kery, M.B., Myers, B.A.: Interactions for untangling messy history in a computational notebook. In: Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing, pp. 147–155 (2018)

    Google Scholar 

  19. Head, A., Hohman, F., Barik, T., Drucker, S.M., DeLine, R.: Managing messes in computational notebooks. In: Human Factors in Computing Systems, pp. 1–12 (2019)

    Google Scholar 

  20. Singer, J.: Notes on notebooks: is Jupyter the bringer of jollity? In: Proceedings of the ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, pp. 180–186 (2020)

    Google Scholar 

  21. Nielsen, J.: Usability Engineering. Morgan Kaufmann Publishers, Burlington (1993)

    Book  MATH  Google Scholar 

  22. Kuusela, H., Paul, P.: A comparison of concurrent and retrospective verbal protocol analysis. Am. J. Psychol. 113(3), 387–404 (2000)

    Article  Google Scholar 

  23. Bosch, J., Holmström Olsson, H., Crnkovic, I.: Engineering AI systems: a research agenda. In: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, pp. 1–19. IGI Global (2021)

    Google Scholar 

  24. Tapia, P., Palacios, E., Noël, L., et al.: Implementing Operational AI in telecom environments. Tupl White Paper 7 (2018)

    Google Scholar 

  25. Fitzgerald, B., Stol, K.J.: Continuous software engineering: a roadmap and agenda. J. Syst. Softw. 123, 176–189 (2017)

    Article  Google Scholar 

  26. Jabbari, R., Ali, N., Petersen, K., Tanveer, B.: Towards a benefits dependency network for DevOps based on a systematic literature review. J. Softw. Evol. Process 30(11), e1957 (2018)

    Article  Google Scholar 

  27. Treveil, M., et al.: Introducing MLOps. O’Reilly Media Inc., Sebastopol (2020)

    Google Scholar 

  28. Hummer, W., et al.: ModelOps: cloud-based lifecycle management for reliable and trusted AI. In: Proceedings of the International Conference on Cloud Engineering, pp. 113–120 (2019)

    Google Scholar 

  29. Borg, M., et al.: Safely entering the deep: a review of verification and validation for machine learning and a challenge elicitation in the automotive industry. J. Automot. Softw. Eng. 1(1), 1–19 (2019)

    Article  Google Scholar 

  30. Falcini, F., Lami, G., Costanza, A.M.: Deep learning in automotive software. IEEE Softw. 34(3), 56–63 (2017)

    Article  Google Scholar 

  31. Jiang, F., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)

    Article  Google Scholar 

  32. Vidot, G., Gabreau, C., Ober, I., Ober, I.: Certification of embedded systems based on machine learning: a survey. arXiv preprint arXiv:2106.07221 (2021)

  33. High-Level Expert Group on Artificial Intelligence: Ethics guidelines for trustworthy artificial intelligence. Technical report, European Commission, Brussels, Belgium (2019)

    Google Scholar 

  34. Borg, M., Englund, C., Duran, B.: Traceability and deep learning-safety-critical systems with traces ending in deep neural networks. In: Proceedings of the Grand Challenges of Traceability: The Next Ten Years, pp. 48–49 (2017)

    Google Scholar 

  35. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: Proceedings of the 5th International Conference on Data Science and Advanced Analytics, pp. 80–89 (2018)

    Google Scholar 

  36. European Commission: Proposal for a Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts, 21 April 2021. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX

  37. European Commission: Regulation (EU) 2016/679 of the European Parliament and of the Council on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/ec (General Data Protection Regulation). Off. J. Eur. Union 119, 1–88 (2016)

    Google Scholar 

  38. Borg, M., Jabangwe, R., Åberg, S., Ekblom, A., Hedlund, L., Lidfeldt, A.: Test automation with Grad-CAM heatmaps - a future pipe segment in MLOps for vision AI? In: Proceedings of the 14th International Conference on Software Testing, Verification and Validation Workshops, pp. 175–181 (2021)

    Google Scholar 

  39. D’Amour, A., et al.: Underspecification presents challenges for credibility in modern machine learning. arXiv preprint arXiv:2011.03395 (2020)

  40. Ahmad, K., Bano, M., Abdelrazek, M., Arora, C., Grundy, J.: What’s up with requirements engineering for artificial intelligence systems? In: Proceedings of the 29th International Requirements Engineering Conference, pp. 1–12 (2021)

    Google Scholar 

  41. Habibullah, K.M., Horkoff, J.: Non-functional requirements for machine learning: understanding current use and challenges in industry. In: Proceedings of the 29th International Requirements Engineering Conference, pp. 13–23 (2021)

    Google Scholar 

  42. Siebert, J., et al.: Construction of a quality model for machine learning systems. Softw. Qual. J., 1–29 (2021)

    Google Scholar 

  43. Zhang, J.M., Harman, M., Ma, L., Liu, Y.: Machine learning testing: survey, landscapes and horizons. IEEE Trans. Softw. Eng. (2020)

    Google Scholar 

  44. Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., Tonella, P.: Testing machine learning based systems: a systematic mapping. Empir. Softw. Eng. 25(6), 5193–5254 (2020)

    Article  Google Scholar 

  45. Xiang, W., et al.: Verification for machine learning, autonomy, and neural networks survey. arXiv preprint arXiv:1810.01989 (2018)

  46. Bjarnason, E., et al.: Challenges and practices in aligning requirements with verification and validation: a case study of six companies. Empir. Softw. Eng. 19(6), 1809–1855 (2014)

    Article  Google Scholar 

  47. Bjarnason, E.: Integrated Requirements Engineering - Understanding and Bridging Gaps in Software Development. Lund University, Sweden (2013). https://lucris.lub.lu.se/ws/portalfiles/portal/3427902/4117182.pdf

  48. Cleland-Huang, J., Marrero, W., Berenbach, B.: Goal-centric traceability: using virtual plumblines to maintain critical systemic qualities. IEEE Trans. Software Eng. 34(5), 685–699 (2008)

    Article  Google Scholar 

Download references

Acknowledgements

Martin Jakobsson and Johan Henriksson are the co-creators of the solution presented in Sect. 2 and deserve all credit for this work. Our thanks go to Backtick Technologies for hosting the MSc thesis project and Dr. Niklas Fors, Dept. of Computer Science, Lund University for acting as the examiner. This initiative received financial support through the AIQ Meta-Testbed project funded by Kompetensfonden at Campus Helsingborg, Lund University, Sweden and two internal RISE initiatives, i.e., “SODA - Software & Data Intensive Applications” and “MLOps by RISE.”

Author information

Authors and Affiliations

  1. RISE Research Institutes of Sweden, Lund, Sweden

    Markus Borg

  2. Department of Computer Science, Lund University, Lund, Sweden

    Markus Borg

Authors
  1. Markus Borg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Markus Borg .

Editor information

Editors and Affiliations

  1. Gdańsk University of Technology, Gdańsk, Poland

    Adam Przybyłek

  2. Gdańsk University of Technology, Gdańsk, Poland

    Aleksander Jarzębowicz

  3. University of Belgrade, Belgrade, Serbia

    Ivan Luković

  4. Nicolaus Copernicus University, Toruń, Poland

    Yen Ying Ng

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Borg, M. (2022). Agility in Software 2.0 – Notebook Interfaces and MLOps with Buttresses and Rebars. In: Przybyłek, A., Jarzębowicz, A., Luković, I., Ng, Y.Y. (eds) Lean and Agile Software Development. LASD 2022. Lecture Notes in Business Information Processing, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-030-94238-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-94238-0_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-94237-3

  • Online ISBN: 978-3-030-94238-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation