Abstract
Many modern software products embed AI components. As a result, their development requires multidisciplinary teams with diverse skill sets. Diversity may lead to communication issues or misapplication of best practices. Process models, which prescribe how software should be developed within an organization, can alleviate this problem. In this paper, we introduce a domain-specific language for modeling AI engineering processes. The DSL concepts stem from our analysis of scientific and gray literature that describes how teams are developing AI-based software. This DSL contributes a structured framework and a common ground for designing, enacting and automating AI engineering processes.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akkiraju, R., et al.: Characterizing machine learning processes: a maturity framework. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 17–31. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_2
Amershi, S., et al.: Software engineering for machine learning: a case study. In: ICSE-SEIP, pp. 291–300 (2019)
Anthes, G.: Artificial intelligence poised to ride a new wave. Commun. ACM 60(7), 19–21 (2017)
Ashmore, R., Calinescu, R., Paterson, C.: Assuring the machine learning lifecycle: desiderata, methods, and challenges. ACM Comput. Surv. 54(5), 1–39 (2021)
CRISP-DM. https://cordis.europa.eu/project/id/25959. Accessed 6 June 2022
Deng, L.: Artificial intelligence in the rising wave of deep learning: the historical path and future outlook. IEEE Signal Proc. Mag. 35(1), 180–187 (2018)
García-Borgoñón, L., Barcelona, M., García-García, J., Alba, M., Escalona, M.: Software process modeling languages: a systematic literature review. Inf. Softw. Technol. 56(2), 103–116 (2014)
Hill, C., Bellamy, R., Erickson, T., Burnett, M.: Trials and tribulations of developers of intelligent systems: a field study. In: 2016 IEEE Symposium on VL/HCC, pp. 162–170 (2016)
IBM Ai Model Lifecycle Management. https://www.ibm.com/blogs/academy-of-technology/ai-model-lifecycle-management-white-paper. Accessed 6 June 2022
Nascimento, E.D.S., Ahmed, I., Oliveira, E., Palheta, M.P., Steinmacher, I., Conte, T.: Understanding development process of machine learning systems: challenges and solutions. In: ESEM 2019, pp. 1–6 (2019)
Olston, C., Reed, B., Srivastava, U., Kumar, R., Tomkins, A.: Pig latin: a not-so-foreign language for data processing. In: SIGMOD, pp. 1099–1110. ACM (2008)
Publio, G.C., et al.: ML-schema: exposing the semantics of machine learning with schemas and ontologies. arXiv preprint arXiv:1807.05351 (2018)
Sujeeth, A.K., et al.: OptiML: an implicitly parallel domain-specific language for machine learning. In: ICML, pp. 609–616 (2011)
What is the Team Data Science Process? https://docs.microsoft.com/en-us/azure/architecture/data-science-process/overview. Accessed 6 June 2022
Wan, Z., Xia, X., Lo, D., Murphy, G.C.: How does machine learning change software development practices? IEEE Trans. Softw. Eng. 47(9), 1857–1871 (2021)
Weimer, M., Condie, T., Ramakrishnan, R., et al.: Machine learning in ScalOps, a higher order cloud computing language. In: NIPS, vol. 9, pp. 389–396 (2011)
Zhao, T., Huang, X.: Design and implementation of DeepDSL: a DSL for deep learning. Comput. Lang. Syst. Struct. 54, 39–70 (2018)
Zucker, J., d’Leeuwen, M.: Arbiter: a domain-specific language for ethical machine learning. In: AAAI/ACM Conference on AI, Ethics, and Society, pp. 421–425 (2020)
Acknowledgements
This work has been partially funded by the Spanish government (PID2020-114615RB-I00/AEI/10.13039/501100011033, project LOCOSS) and the AIDOaRt project, which has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 101007350. The JU receives support from the European Union‘s Horizon 2020 research and innovation programme and Sweden, Austria, Czech Republic, Finland, France, Italy and Spain.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Morales, S., Clarisó, R., Cabot, J. (2022). Towards a DSL for AI Engineering Process Modeling. In: Taibi, D., Kuhrmann, M., Mikkonen, T., Klünder, J., Abrahamsson, P. (eds) Product-Focused Software Process Improvement. PROFES 2022. Lecture Notes in Computer Science, vol 13709. Springer, Cham. https://doi.org/10.1007/978-3-031-21388-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-21388-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21387-8
Online ISBN: 978-3-031-21388-5
eBook Packages: Computer ScienceComputer Science (R0)