AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, 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.
ACM Transactions on Software Engineering and Methodology
Nummer
2
Seiten
1--59
Band
31
eprint
2105.01984
file
:C$\backslash$:/Users/JREB/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Martínez-Fernández et al. - 2022 - Software Engineering for AI-Based Systems A Survey.pdf:pdf
%0 Journal Article
%1 Martinez-Fernandez2022
%A Martínez-Fernández, Silverio
%A Bogner, Justus
%A Franch, Xavier
%A Oriol, Marc
%A Siebert, Julien
%A Trendowicz, Adam
%A Vollmer, Anna Maria
%A Wagner, Stefan
%D 2022
%J ACM Transactions on Software Engineering and Methodology
%K iste-se myown
%N 2
%P 1--59
%R 10.1145/3487043
%T Software Engineering for AI-Based Systems: A Survey
%U http://arxiv.org/abs/2105.01984 https://dl.acm.org/doi/10.1145/3487043
%V 31
%X AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, 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.
@article{Martinez-Fernandez2022,
abstract = {AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, 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.},
added-at = {2022-06-17T09:44:50.000+0200},
archiveprefix = {arXiv},
arxivid = {2105.01984},
author = {Mart{\'{i}}nez-Fern{\'{a}}ndez, Silverio and Bogner, Justus and Franch, Xavier and Oriol, Marc and Siebert, Julien and Trendowicz, Adam and Vollmer, Anna Maria and Wagner, Stefan},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/250dfb3ddeda58e5cc9968a5be5827470/justusbogner},
doi = {10.1145/3487043},
eprint = {2105.01984},
file = {:C$\backslash$:/Users/JREB/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mart{\'{i}}nez-Fern{\'{a}}ndez et al. - 2022 - Software Engineering for AI-Based Systems A Survey.pdf:pdf},
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intrahash = {50dfb3ddeda58e5cc9968a5be5827470},
issn = {1049-331X},
journal = {ACM Transactions on Software Engineering and Methodology},
keywords = {iste-se myown},
month = apr,
number = 2,
pages = {1--59},
timestamp = {2022-11-03T09:31:40.000+0100},
title = {{Software Engineering for AI-Based Systems: A Survey}},
url = {http://arxiv.org/abs/2105.01984 https://dl.acm.org/doi/10.1145/3487043},
volume = 31,
year = 2022
}