理解机器学习框架中系统相关问题的本质：探索性研究
Understanding the Nature of System-Related Issues in Machine Learning Frameworks: An Exploratory Study
摘要：
现代系统是使用开发框架构建的。这些框架对最终系统的执行方式、配置管理方式、测试方式以及部署方式和部署位置有着重大影响。机器学习（ML）框架和使用它们开发的系统与传统框架有很大不同。当然，在这些框架中出现的问题也可能不同-开发人员解决这些问题的行为也可能不同。我们有兴趣描述ML框架中出现的与系统相关的问题-影响性能，内存和资源使用以及其他质量属性的问题，以及它们与传统框架中的问题有何不同。我们进行了一项中等规模的探索性研究，分析了10个流行的机器学习框架中与现实世界系统相关的问题。我们的研究结果为机器学习系统的开发提供了启示，包括某些问题类型发生频率的差异，关于辩论和时间对问题纠正的影响的观察，以及开发人员专业化的差异。我们希望这种探索性的研究将使开发人员能够提高他们的期望，计划风险，并相应地分配资源时，利用这些框架提供的工具来开发基于ML的系统。
英文摘要：
Modern systems are built using development frameworks. These frameworks have a major impact on how the resulting system executes, how configurations are managed, how it is tested, and how and where it is deployed. Machine learning (ML) frameworks and the systems developed using them differ greatly from traditional frameworks. Naturally, the issues that manifest in such frameworks may differ as well---as may the behavior of developers addressing those issues. We are interested in characterizing the system-related issues---issues impacting performance, memory and resource usage, and other quality attributes---that emerge in ML frameworks, and how they differ from those in traditional frameworks. We have conducted a moderate-scale exploratory study analyzing real-world system-related issues from 10 popular machine learning frameworks. Our findings offer implications for the development of machine learning systems, including differences in the frequency of occurrence of certain issue types, observations regarding the impact of debate and time on issue correction, and differences in the specialization of developers. We hope that this exploratory study will enable developers to improve their expectations, plan for risk, and allocate resources accordingly when making use of the tools provided by these frameworks to develop ML-based systems.
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。
