Component-based Approach to Software Engineering of Machine Learning-enabled Systems
Paper i proceeding, 2024

Machine Learning (ML)-enabled systems capture new frontiers of industrial use. The development of such systems is becoming a priority course for many vendors due to the unique capabilities of Artificial Intelligence (AI) techniques. The current trend today is to integrate ML functionality into complex systems as architectural components. There are a lot of relevant challenges associated with this strategy in terms of the overall system architecture and in the context of development workflow (MLOps). The probabilistic nature, crucial dependency on data, and work in an environment of high uncertainty do not allow software engineers to apply traditional software development methodologies. As a result, there is a community request to systematize the most relevant experience in building software architectures with ML components, to create new approaches to organizing the process of developing ML-enabled systems, and to build new models for assessing the system quality. Our research contributes to all mentioned directions and aims to create a methodology for the efficient implementation of ML-enabled software and AI components. The results of the research can be used in the design and development in industrial settings, as well as a basis for further studies in the research field, which is of both practical and scientific value.

machine learning

software quality

software architecture


Vladislav Indykov

Göteborgs universitet

Software Engineering 2

Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024

9798400705915 (ISBN)

3rd International Conference on AI Engineering, CAIN 2024, co-located with the 46th International Conference on Software Engineering, ICSE 2024
Lisbon, Portugal,



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