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Fri 2 May 2025 16:15 - 16:30 at 206 plus 208 - Human and Social for AI Chair(s): Ramiro Liscano

Deep Learning (DL) systems have been widely adopted across various industrial domains such as autonomous driving and intelligent healthcare. As with traditional software, DL systems also need to constantly evolve to meet ever-changing user requirements. However, ensuring the quality of these continuously evolving systems presents significant challenges, especially in the context of testing. Understanding how industry developers address these challenges and what extra obstacles they are facing could provide valuable insights for further safeguarding the quality of DL systems. To reach this goal, we conducted semi-structured interviews with 22 DL developers from diverse domains and backgrounds. More specifically, our study focuses on exploring the challenges developers encounter in testing evolving DL systems, the practical solutions they employ, and their expectations for extra support. Our results highlight the difficulties in testing evolving DL systems and identify the best practices for DL developers to address them. Additionally, we pinpoint potential future research directions to enhance testing effectiveness in evolving DL systems.

Fri 2 May

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:30
Human and Social for AIResearch Track / SE in Society (SEIS) / SE In Practice (SEIP) at 206 plus 208
Chair(s): Ramiro Liscano Ontario Tech University
16:00
15m
Talk
ChatGPT Inaccuracy Mitigation during Technical Report Understanding: Are We There Yet?
Research Track
Salma Begum Tamanna University of Calgary, Canada, Gias Uddin York University, Canada, Song Wang York University, Lan Xia IBM, Canada, Longyu Zhang IBM, Canada
16:15
15m
Talk
Navigating the Testing of Evolving Deep Learning Systems: An Exploratory Interview Study
Research Track
Hanmo You Tianjin University, Zan Wang Tianjin University, Bin Lin Hangzhou Dianzi University, Junjie Chen Tianjin University
16:30
15m
Talk
An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AIArtifact-Available
SE In Practice (SEIP)
Lekshmi Murali Rani Chalmers University of Technology and University of Gothenburg, Sweden, Faezeh Mohammadi Chalmers University of Technology and University of Gothenburg, Sweden, Robert Feldt Chalmers | University of Gothenburg, Richard Berntsson Svensson Chalmers | University of Gothenburg
Pre-print
16:45
15m
Talk
Curious, Critical Thinker, Empathetic, and Ethically Responsible: Essential Soft Skills for Data Scientists in Software Engineering
SE in Society (SEIS)
Matheus de Morais Leça University of Calgary, Ronnie de Souza Santos University of Calgary
17:00
15m
Talk
Multi-Modal LLM-based Fully-Automated Training Dataset Generation Software Platform for Mathematics Education
SE in Society (SEIS)
Minjoo Kim Sookmyung Women's University, Tae-Hyun Kim Sookmyung Women's University, Jaehyun Chung Korea University, Hyunseok Choi Korea University, Seokhyeon Min Korea University, Joon-Ho Lim Tutorus Labs, Soohyun Park Sookmyung Women's University
17:15
15m
Talk
What Does a Software Engineer Look Like? Exploring Societal Stereotypes in LLMs
SE in Society (SEIS)
Muneera Bano CSIRO's Data61, Hashini Gunatilake Monash University, Rashina Hoda Monash University
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