Software Engineering for AI-Based Systems: A Survey

Silverio Martínez-Fernández; Justus Bogner; Xavier Franch; Marc Oriol; Julien Siebert; Adam Trendowicz; Anna Maria Vollmer; Stefan Wagner

doi:10.1145/3487043

Software Engineering for AI-Based Systems: A Survey

Silverio Martínez-Fernández
, Justus Bogner
, Xavier Franch
, Marc Oriol
, Julien Siebert
, Adam Trendowicz
, Anna Maria Vollmer
, Stefan Wagner

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Article number	37e
Journal	ACM Transactions on Software Engineering and Methodology
Volume	31
Issue number	2
DOIs	https://doi.org/10.1145/3487043
Publication status	Published - 1 Apr 2022
Externally published	Yes

Funding

This work has been partially funded by the “Beatriz Galindo” Spanish Program BEAGAL18/00064 and by the DOGO4ML Spanish research project (ref. PID2020-117191RB-I00). We are very grateful to our anonymous reviewers for their comments and suggestions. Authors’ addresses: S. Martínez-Fernández, X. Franch, and M. Oriol, Universitat Politècnica de Catalunya - BarcelonaTech, c/Jordi Girona 1-3, 08034 Barcelona (Spain); emails: [email protected], [email protected], [email protected]; J. Bogner and S. Wagner, University of Stuttgart, Institute of Software Engineering, Germany, Universitätsstraße 38, 70569 Stuttgart (Germany); emails: [email protected], [email protected]; J. Siebert, A, Trendowicz, and A. M. Vollmer, Fraunhofer Institute for Experimental Software Engineering IESE, Fraunhofer-Platz 1, 67663 Kaiserslautern (Germany); emails: [email protected], [email protected], [email protected]. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). © 2022 Copyright held by the owner/author(s). 1049-331X/2022/03-ART37e $15.00 https://doi.org/10.1145/3487043

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title = "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-, 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.",

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Software Engineering for AI-Based Systems: A Survey

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