On the Interaction between Software Engineers and Data Scientists when Building Machine Learning-Enabled Systems
Abstract
Engineering ML-enabled systems presents various challenges from both a theoretical and practical perspective. One of the key challenges is the effective interaction between actors with different backgrounds who need to work closely together, such as software engineers and data scientists. This dissertation involved three studies investigating the current collaboration dynamics between these two roles in ML projects. Our studies revealed several challenges that can hinder collaboration between software engineers and data scientists, including differences in technical expertise and unclear definitions of each role’s duties. Potential solutions to address these challenges include encouraging team communication and producing concise system documentation.
References
Gabriel Busquim, Maria Julia Lima, and Marcos Kalinowski. 2024. On the Interaction between Software Engineers and Data Scientists when Building Machine Learning-Enabled Systems. Master’s thesis. [link]
Gabriel Busquim, Hugo Villamizar, Maria Julia Lima, and Marcos Kalinowski. 2024. On the Interaction between Software Engineers and Data Scientists when Building Machine Learning-Enabled Systems. In International Conference on Software Quality. Springer, 55–75.
Nadia Nahar, Shurui Zhou, Grace Lewis, and Christian Kästner. 2022. Collaboration challenges in building ml-enabled systems: Communication, documentation, engineering, and process. In Proceedings of the 44th international conference on software engineering. 413–425.
