  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.
