Scoping Software Engineering for AI: The TSE Perspective

Advances in Artificial Intelligence (AI), and in particular in Machine Learning (ML), are introducing profound changes to scholarly submissions across publication venues, affecting in particular the contributions that are being submitted to Software Engineering (SE) conferences and journals. In this context, it is not always clear whether manuscripts submitted to SE venues under the umbrella term SE for AI are indeed relevant to SE, in the sense that they explicitly contain contributions to the SE body of knowledge. This leads to recurring discussions on whether certain AI-related submissions are appropriate to SE venues, or should instead be submitted to other journals and conferences, including AI or ML-specific ones. In this editorial, we discuss the kinds of AI-related contributions that are a better fit-and a less good fit-for publication in the IEEE Transactions on Software Engineering.

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Publication Details

Journal
IEEE Transactions on Software Engineering
Published
2024-11-01
DOI
https://doi.org/10.1109/tse.2024.3470368
Citations
5
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
1.74
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article

Scoping Software Engineering for AI: The TSE Perspective

Hao Zhong, Patrizio Pelliccione, Michael Pradel, Nazareno Aguirre et al.
5 citations
IEEE Transactions on Software Engineering
Reinforcement Learning in Robotics
1.74
article

Scoping Software Engineering for AI: The TSE Perspective

Hao Zhong, Patrizio Pelliccione, Michael Pradel, Nazareno Aguirre, Jonathan I. Maletic, Mehrdad Sabetzadeh, Manuel Wimmer, Massimiliano Di Penta, Ana Moreno, Martin Monperrus, LiGuo Huang, Yvonne Dittrich, Leonardo Mariani, Rashina Hoda, Domenico Bianculli, Rahul Purandare, Kelly Blincoe, Sebastián Uchitel, Andy Zaidman, Liliana Pasquale, Filomena Ferrucci, Alexander Serebrenik, Gabriele Bavota, Bram Adams, Tao Yue, Michael R. Lyu, Tim Menzies, Christoph Treude, Marsha Chećhik, David Lo, Chakkrit Tantithamthavorn, Collin McMillan, Sukyoung Ryu, Tao Zhang, Jun Sun, Yingfei Xiong, Lei Ma, Nachiappan Nagappan, Ana Cláudia Rocha Cavalcanti
article en
5 citations

Abstract

Advances in Artificial Intelligence (AI), and in particular in Machine Learning (ML), are introducing profound changes to scholarly submissions across publication venues, affecting in particular the contributions that are being submitted to Software Engineering (SE) conferences and journals. In this context, it is not always clear whether manuscripts submitted to SE venues under the umbrella term SE for AI are indeed relevant to SE, in the sense that they explicitly contain contributions to the SE body of knowledge. This leads to recurring discussions on whether certain AI-related submissions are appropriate to SE venues, or should instead be submitted to other journals and conferences, including AI or ML-specific ones. In this editorial, we discuss the kinds of AI-related contributions that are a better fit-and a less good fit-for publication in the IEEE Transactions on Software Engineering.

IEEE Transactions on Software EngineeringVol. 50(11)
University of Stuttgart (DE), University College Dublin (IE), University of Notre Dame (US), University of Nebraska–Lincoln (US), Johannes Kepler University of Linz (AT), University of Salerno (IT), North Carolina State University (US), Kent State University (US), Universidad Nacional de Río Cuarto (AR), University of Ottawa (CA), University of Auckland (NZ), University of Alberta (CA), Korea Advanced Institute of Science and Technology (KR), University of Sannio (IT), Chinese University of Hong Kong (HK), Southern Methodist University (US), Shanghai Jiao Tong University (CN), University of Toronto (CA), University of Luxembourg (LU), Peking University (CN), University of Macau (MO), Queen's University (CA), Universidad de Buenos Aires (AR), Meta (United States) (US), Gran Sasso Science Institute (IT), University of York (GB), Monash University (AU), Università della Svizzera italiana (CH), University of Milano-Bicocca (IT), Singapore Management University (SG), Beihang University (CN), Eindhoven University of Technology (NL), IT University of Copenhagen (DK), KTH Royal Institute of Technology (SE), Delft University of Technology (NL)
Openalex Percentile: Top 13%
Reinforcement Learning in Robotics
1.74
DOIOpen Access PDFOpenAlex
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