arXiv 2402.053342024-02-08cs.SE

论构建机器学习赋能系统中软件工程师与数据科学家之间的交互

On the Interaction between Software Engineers and Data Scientists when building Machine Learning-Enabled Systems

Gabriel Busquim, Hugo Villamizar, Maria Julia Lima, Marcos Kalinowski

AI总结：

本文通过探索性案例研究，揭示软件工程师与数据科学家在ML赋能系统构建中的协作挑战，并提出促进协作的解决方案，以改善跨角色交互。

摘要：

近年来，机器学习（ML）组件日益被整合到各组织的核心系统中。从理论和实践角度来看，工程化此类系统面临诸多挑战。其中一个关键挑战是不同背景的参与者（如软件工程师和数据科学家）需要紧密合作时的有效交互。本文提出一项探索性案例研究，旨在理解这些角色在ML项目中的当前交互与协作动态。我们对一个大型ML赋能系统项目中具有软件工程和数据科学经验的四名从业者进行了半结构化访谈，并采用反思性主题分析对数据进行了分析。我们的研究发现揭示了若干可能阻碍软件工程师与数据科学家之间协作的挑战，包括技术专长差异、各角色职责定义不清，以及缺乏支持ML赋能系统规格说明的文档。我们还指出了应对这些挑战的潜在解决方案，例如培养协作文化、鼓励团队沟通以及生成简洁的系统文档。本研究有助于理解ML项目中软件工程师与数据科学家之间的复杂动态，并为改善此情境下的协作与沟通提供了见解。我们鼓励未来在其他项目中对此交互开展进一步研究。

英文摘要：

In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such 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 paper presents an exploratory case study to understand the current interaction and collaboration dynamics between these roles in ML projects. We conducted semi-structured interviews with four practitioners with experience in software engineering and data science of a large ML-enabled system project and analyzed the data using reflexive thematic analysis. Our findings reveal several challenges that can hinder collaboration between software engineers and data scientists, including differences in technical expertise, unclear definitions of each role's duties, and the lack of documents that support the specification of the ML-enabled system. We also indicate potential solutions to address these challenges, such as fostering a collaborative culture, encouraging team communication, and producing concise system documentation. This study contributes to understanding the complex dynamics between software engineers and data scientists in ML projects and provides insights for improving collaboration and communication in this context. We encourage future studies investigating this interaction in other projects.

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