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Artifact and reference models for generative machine learning frameworks and build systems

Published: 22 November 2021 Publication History

Abstract

Machine learning is a discipline which has become ubiquitous in the last few years. While the research of machine learning algorithms is very active and continues to reveal astonishing possibilities on a regular basis, the wide usage of these algorithms is shifting the research focus to the integration, maintenance, and evolution of AI-driven systems. Although there is a variety of machine learning frameworks on the market, there is little support for process automation and DevOps in machine learning-driven projects. In this paper, we discuss how metamodels can support the development of deep learning frameworks and help deal with the steadily increasing variety of learning algorithms. In particular, we present a deep learning-oriented artifact model which serves as a foundation for build automation and data management in iterative, machine learning-driven development processes. Furthermore, we show how schema and reference models can be used to structure and maintain a versatile deep learning framework. Feasibility is demonstrated on several state-of-the-art examples from the domains of image and natural language processing as well as decision making and autonomous driving.

References

[1]
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, and Michael Isard. 2016. Tensorflow: A system for large-scale machine learning. In 12th $USENIX$ symposium on operating systems design and implementation ($OSDI$ 16). 265–283.
[2]
Angela Barriga, Adrian Rutle, and Rogardt Heldal. 2019. Personalized and Automatic Model Repairing using Reinforcement Learning. In ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). 175–181.
[3]
Angela Barriga, Adrian Rutle, and Rogardt Heldal. 2020. Improving Model Repair through Experience Sharing. Journal of Object Technology, 19, 2 (2020), July, 13:1–21. issn:1660-1769
[4]
M. Berkovich, S. Esch, C. Mauro, J. Leimeister, and H. Krcmar. 2011. Towards an Artifact Model for Requirements to IT-enabled Product Service Systems. In Wirtschaftsinformatik.
[5]
Jean Bézivin, Frédéric Jouault, and Patrick Valduriez. 2004. On the Need for Megamodels. In Proceedings of the OOPSLA/GPCE: Best Practices for Model-Driven Software Development workshop, 19th Annual ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications,(2004). Vancouver, Canada. https://hal.archives-ouvertes.fr/hal-01222947
[6]
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, and Amanda Askell. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
[7]
Manfred Broy. 2018. A logical approach to systems engineering artifacts: semantic relationships and dependencies beyond traceability—from requirements to functional and architectural views. Software & Systems Modeling, 17, 2 (2018), 365–393. isbn:1619-1374 https://doi.org/10.1007/s10270-017-0619-4
[8]
Arvid Butting, Timo Greifenberg, Bernhard Rumpe, and Andreas Wortmann. 2018. On the Need for Artifact Models in Model-Driven Systems Engineering Projects. In Software Technologies: Applications and Foundations, Martina Seidl and Steffen Zschaler (Eds.) (LNCS 10748). Springer, 146–153. http://www.se-rwth.de/publications/On-the-Need-for-Artifact-Models-in-Model-Driven-Systems-Engineering-Projects.pdf
[9]
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G Bellemare. 2018. Dopamine: A research framework for deep reinforcement learning. arXiv preprint arXiv:1812.06110.
[10]
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao. 2015. DeepDriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE international conference on computer vision. 2722–2730.
[11]
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.
[12]
D. Fernández and Birgit Penzenstadler. 2014. Artefact-based requirements engineering: the AMDiRE approach. Requirements Engineering, 20 (2014), 405–434.
[13]
Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, and Design Patterns. 1995. Elements of Reusable Object-Oriented Software. Design Patterns. massachusetts: Addison-Wesley Publishing Company.
[14]
Nicola Gatto, Evgeny Kusmenko, and Bernhard Rumpe. 2019. Modeling Deep Reinforcement Learning Based Architectures for Cyber-Physical Systems. In Proceedings of MODELS 2019. Workshop MDE Intelligence, Loli Burgueño, Alexander Pretschner, Sebastian Voss, Michel Chaudron, Jörg Kienzle, Markus Völter, Sébastien Gérard, Mansooreh Zahedi, Erwan Bousse, Arend Rensink, Fiona Polack, Gregor Engels, and Gerti Kappel (Eds.). 196–202. http://www.se-rwth.de/publications/Modeling-Deep-Reinforcement-Learning-based-Architectures-for-Cyber-Physical-Systems.pdf
[15]
Timo Greifenberg. 2019. Artefaktbasierte Analyse modellgetriebener Softwareentwicklungsprojekte. Shaker Verlag. isbn:978-3-8440-6879-5 http://www.se-rwth.de/phdtheses/Diss-Greifenberg-Artefaktbasierte-Analyse-modellgetriebener-Softwareentwicklungsprojekte.pdf
[16]
Timo Greifenberg, Steffen Hillemacher, and Katrin Hölldobler. 2020. Applied Artifact-Based Analysis for Architecture Consistency Checking. Springer, 61–85. http://www.se-rwth.de/publications/Applied-Artifact-Based-Analysis-for-Architecture-Consistency-Checking.pdf
[17]
Antonio Gulli and Sujit Pal. 2017. Deep learning with Keras. Packt Publishing Ltd.
[18]
Thomas Hartmann, Assaad Moawad, Francois Fouquet, and Yves Le Traon. 2019. The next evolution of MDE: a seamless integration of machine learning into domain modeling. Software & Systems Modeling, 18, 2 (2019), 1285–1304. isbn:1619-1374
[19]
Regina Hebig, Andreas Seibel, and Holger Giese. 2011. On the Unification of Megamodels. In Proceedings of the 4th International Workshop on Multi-Paradigm Modeling (MPM 2010), Vasco Amaral, Hans Vangheluwe, Cécile Hardebolle, Laszlo Lengyel, Tiziana Magaria, Julia Padberg, and Gabriele Taentzer (Eds.) (Electronic Communications of the EASST, Vol. 42). http://journal.ub.tu-berlin.de/eceasst/article/view/704/713
[20]
Brian Henderson-Sellers and Cesar Gonzalez-Perez. 2005. The rationale of powertype-based metamodelling to underpin software development methodologies. In Conferences in Research and Practice in Information Technology Series.
[21]
Steffen Hillemacher, Nicolas Jäckel, Christopher Kugler, Philipp Orth, David Schmalzing, and Louis Wachtmeister. 2021. Artifact-Based Analysis for the Development of Collaborative Embedded Systems. Springer, 315–331. http://www.se-rwth.de/publications/Artifact-Based-Analysis-for-the-Development-of-Collaborative-Embedded-Systems.pdf
[22]
Robert Hirschfeld, Pascal Costanza, and Oscar Marius Nierstrasz. 2008. Context-oriented programming. Journal of Object technology, 7, 3 (2008), 125–151.
[23]
Abhijit Karmarkar, Ahmet Altay, Aleksandr Zaks, Neoklis Polyzotis, Anusha Ramesh, Ben Mathes, Gautam Vasudevan, Irene Giannoumis, Jarek Wilkiewicz, and Jiri Simsa. 2020. Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX). arXiv preprint arXiv:2010.02013.
[24]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
[25]
Jörg Christian Kirchhof, Bernhard Rumpe, David Schmalzing, and Andreas Wortmann. 2022. MontiThings: Model-driven Development and Deployment of Reliable IoT Applications. Journal of Systems and Software, 183 (2022), January, 111087. issn:0164-1212 https://doi.org/10.1016/j.jss.2021.111087
[26]
Thomas Kühn, Max Leuthäuser, Sebastian Götz, Christoph Seidl, and Uwe Aß mann. 2014. A metamodel family for role-based modeling and programming languages. In International Conference on Software Language Engineering. 141–160.
[27]
Alexander Kuhnle, Michael Schaarschmidt, and Kai Fricke. 2017. Tensorforce: a TensorFlow library for applied reinforcement learning. Web page. https://github.com/tensorforce/tensorforce
[28]
Evgeny Kusmenko, Sebastian Nickels, Svetlana Pavlitskaya, Bernhard Rumpe, and Thomas Timmermanns. 2019. Modeling and Training of Neural Processing Systems. In Conference on Model Driven Engineering Languages and Systems (MODELS’19), Marouane Kessentini, Tao Yue, Alexander Pretschner, Sebastian Voss, and Loli Burgueño (Eds.). IEEE, 283–293. http://www.se-rwth.de/publications/Modeling-and-Training-of-Neural-Processing-Systems.pdf
[29]
Evgeny Kusmenko, Svetlana Pavlitskaya, Bernhard Rumpe, and Sebastian Stüber. 2019. On the Engineering of AI-Powered Systems. In ASE’19. Software Engineering Intelligence Workshop (SEI’19), Lisa O’Conner (Ed.). IEEE, 126–133. http://www.se-rwth.de/publications/On-the-Engineering-of-AI-Powered-Systems.pdf
[30]
Evgeny Kusmenko, Alexander Roth, Bernhard Rumpe, and Michael von Wenckstern. 2017. Modeling Architectures of Cyber-Physical Systems. In European Conference on Modelling Foundations and Applications (ECMFA’17) (LNCS 10376). Springer, 34–50. http://www.se-rwth.de/publications/Modeling-Architectures-of-Cyber-Physical-Systems.pdf
[31]
Evgeny Kusmenko, Bernhard Rumpe, Sascha Schneiders, and Michael von Wenckstern. 2018. Highly-Optimizing and Multi-Target Compiler for Embedded System Models: C++ Compiler Toolchain for the Component and Connector Language EmbeddedMontiArc. In Conference on Model Driven Engineering Languages and Systems (MODELS’18). ACM, 447 – 457. http://www.se-rwth.de/publications/Highly-Optimizing-and-Multi-Target-Compiler-for-Embedded-System-Models.pdf
[32]
Jeremy Lacomis, Pengcheng Yin, Edward Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, and Bogdan Vasilescu. 2019. DIRE: A Neural Approach to Decompiled Identifier Naming. In 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). 628–639.
[33]
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proc. IEEE, 86, 11 (1998), 2278–2324.
[34]
Yann LeCun, Corinna Cortes, and CJ Burges. 2010. MNIST handwritten digit database. ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist, 2 (2010).
[35]
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
[36]
Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.
[37]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, and Georg Ostrovski. 2015. Human-level control through deep reinforcement learning. nature, 518, 7540 (2015), 529–533.
[38]
Phuong T. Nguyen, Juri Di Rocco, Davide Di Ruscio, Alfonso Pierantonio, and Ludovico Iovino. 2019. Automated Classification of Metamodel Repositories: A Machine Learning Approach. In ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). 272–282. https://doi.org/10.1109/MODELS.2019.00011
[39]
Phuong T. Nguyen, Davide Di Ruscio, Alfonso Pierantonio, Juri Di Rocco, and Ludovico Iovino. 2021. Convolutional neural networks for enhanced classification mechanisms of metamodels. Journal of Systems and Software, 172 (2021), 110860. issn:0164-1212
[40]
Morgan Quigley, Brian Gerkey, Ken Conley, Josh Faust, Tully Foote, Jeremy Leibs, Eric Berger, Rob Wheeler, and Andrew Ng. 2009. ROS: an open-source Robot Operating System. In Proc. of the IEEE Intl. Conf. on Robotics and Automation (ICRA) Workshop on Open Source Robotics.
[41]
Mark Richters and Martin Gogolla. 2000. Validating UML models and OCL constraints. In International Conference on the Unified Modeling Language. 265–277.
[42]
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28 (2015), 2503–2511.
[43]
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.
[44]
Friedrich Steimann. 2000. On the representation of roles in object-oriented and conceptual modelling. Data & Knowledge Engineering, 35, 1 (2000), 83–106.
[45]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762.
[46]
Yao Wan, Jingdong Shu, Yulei Sui, Guandong Xu, Zhou Zhao, Jian Wu, and Philip S. Yu. 2019. Multi-Modal Attention Network Learning for Semantic Source Code Retrieval. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE ’19). IEEE Press, 13–25. isbn:9781728125084 https://doi.org/10.1109/ASE.2019.00012
[47]
Bernhard Wymann, Eric Espié, Christophe Guionneau, Christos Dimitrakakis, Rémi Coulom, and Andrew Sumner. 2000. TORCS, the open racing car simulator. Software available at http://torcs. sourceforge. net, 4, 6 (2000), 2.
[48]
Matei Zaharia, Andrew Chen, Aaron Davidson, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, and Mani Parkhe. 2018. Accelerating the Machine Learning Lifecycle with MLflow. IEEE Data Eng. Bull., 41, 4 (2018), 39–45.

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cover image ACM Conferences
GPCE 2021: Proceedings of the 20th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences
October 2021
209 pages
ISBN:9781450391122
DOI:10.1145/3486609
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 the author(s) 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].

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Published: 22 November 2021

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Author Tags

  1. artifact models
  2. artificial intelligence
  3. build systems
  4. compiler
  5. machine learning
  6. metamodeling
  7. reference models
  8. training

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GPCE '21
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GPCE '21: Concepts and Experiences
October 17 - 18, 2021
IL, Chicago, USA

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  • (2024)Model driven engineering for machine learning componentsInformation and Software Technology10.1016/j.infsof.2024.107423169:COnline publication date: 2-Jul-2024
  • (2024)Bridging MDE and AI: a systematic review of domain-specific languages and model-driven practices in AI software systems engineeringSoftware and Systems Modeling10.1007/s10270-024-01211-yOnline publication date: 28-Sep-2024
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  • (2022)EMMM: A Unified Meta-Model for Tracking Machine Learning Experiments2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA56994.2022.00016(48-55)Online publication date: Aug-2022

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