arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2007.14130cs.SE
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软件工程最佳实践在机器学习中的应用和效果

Adoption and Effects of Software Engineering Best Practices in Machine Learning

Alex Serban, Koen van der Blom, Holger Hoos, Joost Visser

摘要:

越来越多地依赖于具有机器学习(ML)组件的应用程序,需要成熟的工程技术来确保这些组件以稳健和面向未来的方式构建。我们的目标是凭经验确定团队如何使用ML组件开发、部署和维护软件的最新技术水平。我们挖掘了学术和灰色文献,并确定了29个ML应用的工程最佳实践。我们对313名从业者进行了调查,以确定这些做法的采用程度,并验证其感知效果。使用调查响应,我们量化了实践采用,并根据人口统计特征(如地理或团队规模)进行了区分。我们还测试了相关性,并使用各种统计模型研究了实践与其感知效果之间的线性和非线性关系。例如,我们的研究结果表明,较大的团队倾向于采用更多的实践,而传统的软件工程实践的采用率往往低于ML特定的实践。此外,统计模型可以准确地预测感知效果,如敏捷性,软件质量和可追溯性,从采用特定的实践集的程度。结合实践采用率与实践的重要性,如统计模型所揭示的,我们确定了重要但采用率低的实践,以及被广泛采用但对我们研究的效果不太重要的实践。总的来说,我们的调查和对收到的回复的分析为ML团队评估和逐步改进实践提供了定量基础。

英文摘要:

The increasing reliance on applications with machine learning (ML) components calls for mature engineering techniques that ensure these are built in a robust and future-proof manner. We aim to empirically determine the state of the art in how teams develop, deploy and maintain software with ML components. We mined both academic and grey literature and identified 29 engineering best practices for ML applications. We conducted a survey among 313 practitioners to determine the degree of adoption for these practices and to validate their perceived effects. Using the survey responses, we quantified practice adoption, differentiated along demographic characteristics, such as geography or team size. We also tested correlations and investigated linear and non-linear relationships between practices and their perceived effect using various statistical models. Our findings indicate, for example, that larger teams tend to adopt more practices, and that traditional software engineering practices tend to have lower adoption than ML specific practices. Also, the statistical models can accurately predict perceived effects such as agility, software quality and traceability, from the degree of adoption for specific sets of practices. Combining practice adoption rates with practice importance, as revealed by statistical models, we identify practices that are important but have low adoption, as well as practices that are widely adopted but are less important for the effects we studied. Overall, our survey and the analysis of responses received provide a quantitative basis for assessment and step-wise improvement of practice adoption by ML teams.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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