arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2012.07919cs.SEcs.LG
收藏

工程机器学习系统的软件工程视角:技术现状与挑战

A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges

Görkem Giray

摘要:

内容:机器学习(ML)的进步导致从传统的软件开发观点(算法由人类硬编码)转变为通过从数据中学习实现的ML系统。因此,我们需要重新审视我们开发软件系统的方法,并考虑这些新类型系统所需的特殊性。目的:本研究的目的是系统地识别,分析,总结和综合的软件工程(SE)研究的工程ML系统的当前状态。方法:我进行了系统性文献综述(SLR)。我系统地选择了141个研究池从SE场馆,然后进行了定量和定性分析,从这些研究中提取的数据。结果:ML系统的非确定性本质使工程ML系统的所有SE方面变得复杂。尽管从2018年开始,人们的兴趣越来越大,但结果显示,SE方面都没有一套成熟的工具和技术。测试是迄今为止最受研究人员欢迎的领域。即使是测试机器学习系统,工程师也只有一些工具原型和解决方案,实验证明也很薄弱。ML系统工程的许多挑战都是通过调查和访谈确定的。研究人员应该进行实验和案例研究,最好是在工业环境中进行,以进一步了解这些挑战并提出解决方案。结论:这些结果可能有利于(1)从业者应对ML系统工程的挑战;(2)研究人员和学者识别潜在的研究问题;(3)教育工作者设计或更新SE课程以涵盖ML系统工程。

英文摘要:

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of software development, where algorithms are hard-coded by humans, to ML systems materialized through learning from data. Therefore, we need to revisit our ways of developing software systems and consider the particularities required by these new types of systems. Objective: The purpose of this study is to systematically identify, analyze, summarize, and synthesize the current state of software engineering (SE) research for engineering ML systems. Method: I performed a systematic literature review (SLR). I systematically selected a pool of 141 studies from SE venues and then conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: The non-deterministic nature of ML systems complicates all SE aspects of engineering ML systems. Despite increasing interest from 2018 onwards, the results reveal that none of the SE aspects have a mature set of tools and techniques. Testing is by far the most popular area among researchers. Even for testing ML systems, engineers have only some tool prototypes and solution proposals with weak experimental proof. Many of the challenges of ML systems engineering were identified through surveys and interviews. Researchers should conduct experiments and case studies, ideally in industrial environments, to further understand these challenges and propose solutions. Conclusion: The results may benefit (1) practitioners in foreseeing the challenges of ML systems engineering; (2) researchers and academicians in identifying potential research questions; and (3) educators in designing or updating SE courses to cover ML systems engineering.

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。

↑