Software Engineering for AI-Based Systems: A Survey. Martínez-Fernández, S., Bogner, J., Franch, X., Oriol, M., Siebert, J., Trendowicz, A., Vollmer, A. M., & Wagner, S. ACM Transactions on Software Engineering and Methodology, 31(2):1–59, April, 2022. ![Software Engineering for AI-Based Systems: A Survey [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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-fernandez_software_2022,
title = {Software {Engineering} for {AI}-{Based} {Systems}: {A} {Survey}},
volume = {31},
issn = {1049-331X, 1557-7392},
shorttitle = {Software {Engineering} for {AI}-{Based} {Systems}},
url = {https://dl.acm.org/doi/10.1145/3487043},
doi = {10.1145/3487043},
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.},
language = {en},
number = {2},
urldate = {2024-08-06},
journal = {ACM Transactions on Software Engineering and Methodology},
author = {Martínez-Fernández, Silverio and Bogner, Justus and Franch, Xavier and Oriol, Marc and Siebert, Julien and Trendowicz, Adam and Vollmer, Anna Maria and Wagner, Stefan},
month = apr,
year = {2022},
pages = {1--59},
file = {Submitted Version:/Users/ra79rew/Zotero/storage/5T6HPNKZ/Martínez-Fernández et al. - 2022 - Software Engineering for AI-Based Systems A Surve.pdf:application/pdf},
}
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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. 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