On the Interaction between Software Engineers and Data Scientists when Building Machine Learning-Enabled Systems - Busquim, Gabriel; Lima, Maria Julia; Kalinowski, Marcos (Anais do Simpósio Brasileiro de Qualidade de Software (SBQS); 2025: Anais do XXIV Simpósio Brasileiro de Qualidade de Software, 2025)
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.