Federica Razzano

She is pursuing an industrial PhD in collaboration with Engineering Ingegneria Informatica S.p.A, bridging academic research and industrial practice in the deployment and operation of software systems. Within this collaboration, she contributes to research on TOSCA-based orchestration of cloud-native applications.

Her research focuses on Machine Learning Operations (MLOps), combining qualitative and quantitative empirical methods, including practitioner interviews, surveys, and mining software repositories, she studies how MLOps is adopted in industrial and open-source settings, which challenges teams face, and how the available tooling supports them.

In particular, she has characterized how popular open-source MLOps frameworks are used by GitHub projects and which enhancements their users request, in a study published at MSR 2026. Building on interviews with industry practitioners, she has also identified distinct archetypes of MLOps adoption and a taxonomy of the technical and socio-technical challenges associated with each of them.

She is also interested in the human and social aspects of software development, such as community smells in open-source communities.