The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Engineering AI Systems: A Research Agenda
Abstract
Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry. However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this chapter, the authors provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that they have studied. The main contribution of the chapter is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.
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In the citing paper
“Achieving a low inference latency and high throughput is widely reported as a challenge in serving ML models [6,7,12,28,34].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“Edge deployments are often reported as being more challenging than cloud deployments because of hardware resource heterogeneity, the typically distributed nature of edge devices, and possibly varying network connection quality [7,10,20,22,39,53].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“Serving-related challenges are some of the ones most often reported in the literature [5-7, 9, 11, 12, 16, 20, 28, 34, 44, 53, 54], usually from a performance perspective.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“The authors also highlight the integration of data-driven models with computationdriven software components as a potentially non-trivial task.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“[7] reported the extensive use of glue code as a challenge with which companies struggle.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“Monitoring ML models is challenging [6,28,41], but is required in order to detect performance degradations and build confidence in the models [7,10].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
In the citing paper
“[7] and [42] reported a lack of generic solutions for deploying ML models to embedded and edge devices.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [7]
Citations
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A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks
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Software Engineering Challenges of Deep Learning
Arpteg, A., Brinne, B., Crnkovic-Friis, L., & Bosch, J. (2018) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa.2018.00018 · Cited by 238
Self-citation
Research works printed but not linked: 1
- R. A.Munappy, 2020, Modeling Data Pipelines., Proceedings of the Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
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Automated extraction and parsing of key information in complex substation drawings
Yang, J., Chen, Y., Qian, C., Wang, Y., Shen, X., & Zeng, Y. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08271-3
Cloud-based AI Solutions for Smart Engineering Platforms
Dass, A. K., Panigrahi, S., & Sahu, S. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898814410126010004
RAD-AI: Rethinking Architecture Documentation for AI-Augmented Ecosystems
Larsen, O. A., & Moghaddam, M. T. (2026) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c68850.2026.00054
Huang, Y., Fossati, S., Scaramuzza, F., Kumara, I., Nucci, D. D., & Tamburri, D. A. (2026) · IEEE International Conference on Software Architecture (ICSA) · doi:10.1109/icsa66085.2026.00037
Assisting Developers in the Selection of Generative AI Models
Berenguer-Mueller, R., Cobos, S., Izquierdo, J. L., & Clarisó, R. (2026) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1145/3793653.3793786
LLMs as Design Partners for AI-Based System Patterns: An Empirical Evaluation
de Oliveira, F. R., de Souza, F. V., Barros, A. L., & Maia, P. H. M. (2026) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1145/3793653.3793760
Torres, A., & Fernandez, M. (2024) · Journal of Computational and Data-Driven Materials Engineering · doi:10.68159/z116548786
Wojda, T. R., & Stawicki, S. P. (2026) · Artificial intelligence · doi:10.5772/intechopen.1016859
Metadata
- Authors
- Bosch, J., Olsson, H. H., & Crnkovic, I.
- Affiliations
- Chalmers University of Technology (SE)
- Malmö University (SE)
- Chalmers University of Technology, Sweden
- Venue
- Advances in Systems Analysis, Software Engineering, and High Performance Computing
- Publisher
- IGI Global
- Published
- 2021
- Pages
- 1-19
- ISSN
- 2327-3453 (ISSN-L)
- 2327-3461
- OpenAlex
- W3149731118
- Cited by
- 108
- References
- 26
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 15 · 2023: 12 · 2022: 18 · 2021: 18
- FWCI
- 34.72
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in systems analysis, software engineering, and high performance computing book series (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- AI engineering, machine learning systems, data quality, model performance, compliance, engineering problems, research agenda
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