[论文解读] Software engineering for artificial intelligence and machine learning software: A systematic literature review

Elizamary Nascimento, Anh Nguyen‐Duc+2 more|arXiv (Cornell University)|Nov 7, 2020

Software Engineering Research参考文献 62被引用 41

一句话总结

本论文综述了软件工程在 AI/ML 系统中的应用，识别自 1990–2019 年的实践、背景和关键挑战。

ABSTRACT

Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer. However, developing AI/ML systems has presented several engineering problems that are different from those that arise in, non-AI/ML software development. This study aims to investigate how software engineering (SE) has been applied in the development of AI/ML systems and identify challenges and practices that are applicable and determine whether they meet the needs of professionals. Also, we assessed whether these SE practices apply to different contexts, and in which areas they may be applicable. We conducted a systematic review of literature from 1990 to 2019 to (i) understand and summarize the current state of the art in this field and (ii) analyze its limitations and open challenges that will drive future research. Our results show these systems are developed on a lab context or a large company and followed a research-driven development process. The main challenges faced by professionals are in areas of testing, AI software quality, and data management. The contribution types of most of the proposed SE practices are guidelines, lessons learned, and tools.

研究动机与目标

• 了解将软件工程应用于 AI/ML 系统的现状。
• 识别针对 AI/ML 软件提出的软件工程实践、指南、经验教训和工具。
• 分析应用软件工程实践的情境（实验室与产业等），并评估适用性。
• 突出局限性和待解决的挑战，为未来研究提供指引。

提出的方法

• 对自 1990 到 2019 年发表的 AI/ML 软件工程研究进行系统文献综述。
• 将 SE 实践分类并总结为指南、经验教训或工具。
• 综合不同情境中 SE 实践的背景与适用性。
• 识别局限性与待解决的研究挑战，以引导未来工作。

实验结果

研究问题

• RQ1 已经为 AI/ML 软件开发提出了哪些软件工程实践？
• RQ2 这些 SE 实践在哪些情境（实验室、产业等）中应用，它们的适用性如何？
• RQ3 应用 SE 于 AI/ML 系统时报告的主要挑战和局限性是什么？
• RQ4 文献中以哪种贡献类型占主导（指南、经验教训、工具）？
• RQ5 未来研究应解决哪些差距？

主要发现

• AI/ML 系统往往在实验室环境或大型公司内部开发。
• 开发往往遵循以研究为驱动的过程。
• 主要挑战包括测试、AI 软件质量和数据管理。
• 大多数提出的软件工程实践是指南、经验教训或工具。