Developing ML/DL Models: A design framework
Paper in proceedings, 2020

Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.

Machine Learning

Software Engineering

Artificial Intelligence

Deep Learning



Meenu Mary John

Malmö university

Helena Holmström Olsson

Malmö university

Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers), Software Engineering for Testing, Requirements, Innovation and Psychology

Proceedings - 2020 IEEE/ACM International Conference on Software and System Processes, ICSSP 2020


2020 IEEE/ACM International Conference on Software and System Processes, ICSSP 2020
Virtual, Online, South Korea,

Subject Categories

Other Computer and Information Science

Software Engineering

Computer Systems



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