Software Engineering for AI-Based Systems: A Survey
Abstract
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.
Cite
@article{arxiv.2105.01984,
title = {Software Engineering for AI-Based Systems: A Survey},
author = {Silverio Martínez-Fernández and Justus Bogner and Xavier Franch and Marc Oriol and Julien Siebert and Adam Trendowicz and Anna Maria Vollmer and Stefan Wagner},
journal= {arXiv preprint arXiv:2105.01984},
year = {2022}
}
Comments
Accepted in ACM Transactions on Software Engineering and Methodology (TOSEM). For its published version refer to the Journal of ACM TOSEM