AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.

中文翻译：

---

基于AI的系统的软件工程：调查

基于AI的系统是具有至少一个AI组件启用的功能的软件系统（例如，用于图像和语音识别以及自动驾驶）。随着AI的发展，基于AI的系统正在社会中普及。但是，关于用于构建，操作和维护基于AI的系统的软件工程（SE）方法的综合知识有限。为了收集和分析基于AI的系统的SE的最新知识，我们进行了系统的制图研究。我们考虑了2010年1月至2020年3月之间发表的248项研究。基于AI的系统的SE是一个新兴的研究领域，自2018年以来已发表了超过2/3的研究。基于AI的系统研究最多的属性是可靠性和安全。我们为基于AI的系统确定了多种SE方法，我们根据SWEBOK区域进行了分类。有关软件测试和软件质量的研究非常普遍，而诸如软件维护之类的领域似乎被忽略了。与数据相关的问题是最经常出现的挑战。我们的结果对以下人员而言是有价值的：研究人员可以快速了解最新技术并了解哪些主题需要进一步研究；从业者，以了解SE应用于基于AI的系统的方法和挑战；并且，教育者要在课程中弥合SE和AI之间的鸿沟。快速了解最新技术并了解哪些主题需要进一步研究；从业者，以了解SE应用于基于AI的系统的方法和挑战；并且，教育者要在课程中弥合SE和AI之间的鸿沟。快速了解最新技术并了解哪些主题需要进一步研究；从业者，以了解SE应用于基于AI的系统的方法和挑战；并且，教育者要在课程中弥合SE和AI之间的鸿沟。