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.

中文翻译：

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人工智能和机器学习软件的软件工程：系统文献综述

人工智能 (AI) 或机器学习 (ML) 系统已被所有行业的公司广泛采用作为价值主张，以创建或扩展他们提供的服务和产品。然而，开发 AI/ML 系统已经提出了几个与非 AI/ML 软件开发中出现的问题不同的工程问题。本研究旨在调查软件工程 (SE) 如何应用于 AI/ML 系统的开发，并确定适用的挑战和实践，并确定它们是否满足专业人士的需求。此外，我们评估了这些 SE 实践是否适用于不同的环境，以及它们可能适用于哪些领域。我们对 1990 年至 2019 年的文献进行了系统回顾，以 (i) 理解和总结该领域的当前技术水平，以及 (ii) 分析其局限性和将推动未来研究的开放挑战。我们的结果表明，这些系统是在实验室环境或大公司中开发的，并遵循研究驱动的开发过程。专业人士面临的主要挑战是测试、人工智能软件质量和数据管理领域。大多数提议的 SE 实践的贡献类型是指南、经验教训和工具。AI 软件质量和数据管理。大多数提议的 SE 实践的贡献类型是指南、经验教训和工具。AI 软件质量和数据管理。大多数提议的 SE 实践的贡献类型是指南、经验教训和工具。