Continuous experimentation on artificial intelligence software : a research agenda
Nguyen-Duc, A., & Abrahamsson, P. (2020). Continuous experimentation on artificial intelligence software : a research agenda. In P. Devanbu, M. Cohen, & T. Zimmermann (Eds.), ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1513-1516). ACM. https://doi.org/10.1145/3368089.3417039
© 2020 the Authors and ACM
Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an “unknown unknown” problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.
Parent publication ISBN978-1-4503-7043-1
ConferenceACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Is part of publicationESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Publication in research information system
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