一句话：综述数据科学家的角色与工具需求，并探讨面向数据中心软件重思调试、测试及开放问题。
本主题演讲指出，随着大数据、机器学习与 AI 的发展，现有软件工程技术需要重新构想，以满足开发者对生产力的需求。演讲回顾数据科学家的新兴角色及其构建可扩展、正确、高效的数据中心软件所需工具，并介绍与微软研究院合作、约 800 名数据科学家参与的大规模研究，分析其教育背景、问题主题、工具与活动，归纳出九类数据科学家群体及各自最佳实践与挑战。后半部分讨论软件工程研究方向应如何转向应对数据中心软件开发的新挑战，并给出其团队为 Apache Spark 等大数据分布式系统重新设计调试与测试的案例，最后提出 ML 与异构计算中的开放软件工程问题。
With the development of big data, machine learning, and AI, existing software engineering techniques must be reimagined to provide the productivity gains that developers desire. This talk will review emerging roles of data scientists and the tools they need to build scalable, correct, and efficient software for a data centric world. Kim will present a large-scale study of about 800 data scientists in collaboration with Microsoft Research, which looked at data scientists' educational background, problem topics that they work on, tools they use, and activities. From the gathered data, she has identified nine distinct clusters of data scientists and best practices and challenges faced by each cluster. In the second half of this talk, she will discuss the needs of retargeting SE research community's directions to address new challenges in the era of data-centric software development. In particular, she will detail some examples of her group's work that re-invents debugging and testing for big data distributed systems such as Apache Spark. She will conclude with open SE problems in ML and heterogeneous computing that support data-centric software development.
DOI 原文 ·
@inproceedings{paperbot5180,
title = {Keynotes},
author = {},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00010}
}