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.
%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
%C New York, NY, USA
%D 2022
%I Association for Computing Machinery
%J ACM Trans. Softw. Eng. Methodol.
%K artificial-intelligence iste-se myown software
%N 2
%R 10.1145/3487043
%T Software Engineering for AI-Based Systems: A Survey
%U https://doi.org/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 = {2022-05-16T08:47:53.000+0200},
address = {New York, NY, USA},
articleno = {37e},
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},
bdsk-url-1 = {https://doi.org/10.1145/3487043},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ff4d73bb740aa56c705140fa2a5d8a6a/wagnerst},
date-added = {2022-04-04 09:52:12 +0200},
date-modified = {2022-04-04 09:53:51 +0200},
doi = {10.1145/3487043},
interhash = {86293ea1491ce88030ea9e57a9dc92b5},
intrahash = {ff4d73bb740aa56c705140fa2a5d8a6a},
issn = {1049-331X},
issue_date = {April 2022},
journal = {ACM Trans. Softw. Eng. Methodol.},
keywords = {artificial-intelligence iste-se myown software},
month = {4},
number = 2,
numpages = {59},
publisher = {Association for Computing Machinery},
timestamp = {2022-06-01T06:56:18.000+0200},
title = {Software Engineering for {AI}-Based Systems: A Survey},
url = {https://doi.org/10.1145/3487043},
volume = 31,
year = 2022
}
