Strategies for Reuse and Sharing among Data Scientists in Software Teams

Abstract

Effective sharing and reuse practices have long been hallmarks of proficient software engineering. Yet the exploratory nature of data science presents new challenges and opportunities to support sharing and reuse of analysis code. To better understand current practices, we conducted interviews (N=17) and a survey (N=132) with data scientists at Microsoft, and extract five commonly used strategies for sharing and reuse of past work: personal analysis reuse, personal utility libraries, team shared analysis code, team shared template notebooks, and team shared libraries. We also identify factors that encourage or discourage data scientists from sharing and reusing. Our participants described obstacles to reuse and sharing including a lack of incentives to create shared code, difficulties in making data science code modular, and a lack of tool interoperability. We discuss how future tools might help meet these needs.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.