A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems (ICSME 2024 - Research Track) - ICSME 2024

Sun 6 - Fri 11 October 2024

Who

Federica Pepe, Fiorella Zampetti, Antonio Mastropaolo, Gabriele Bavota, Massimiliano Di Penta

Track

ICSME 2024 Research Track

This program is tentative and subject to change.

Time Zone

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When

Wed 9 Oct 2024 15:30 - 15:45 at Fremont - Session 6: Maintenance of AI-based Systems Chair(s): Sujoy Roychowdhury

Federica Pepe

Fiorella Zampetti

University of Sannio, Italy

Italy

Antonio Mastropaolo

William and Mary, USA

United States

Gabriele Bavota

Software Institute @ Università della Svizzera Italiana

Switzerland

Massimiliano Di Penta

University of Sannio, Italy

Italy

This program is tentative and subject to change.

Time Zone

The program is currently displayed in (GMT-06:00) Mountain Time (US & Canada).

Use conference time zone: (GMT-06:00) Mountain Time (US & Canada)Select other time zone

The GMT offsets shown reflect the offsets at the moment of the conference.

Time Band

By setting a time band, the program will dim events that are outside this time window. This is useful for (virtual) conferences with a continuous program (with repeated sessions).
The time band will also limit the events that are included in the personal iCalendar subscription service.

Display full programSpecify a time band

Session Program

Wed 9 Oct
Displayed time zone: Mountain Time (US & Canada) change

	15:30 - 17:00	Session 6: Maintenance of AI-based SystemsNew Ideas and Emerging Results Track / Research Track / Registered Reports Track at Fremont Chair(s): Sujoy Roychowdhury Ericsson R&D

	15:30 15m		A Taxonomy of Self-Admitted Technical Debt in Deep Learning SystemsResearch Track Paper Research Track Federica Pepe , Fiorella Zampetti University of Sannio, Italy, Antonio Mastropaolo William and Mary, USA, Gabriele Bavota Software Institute @ Università della Svizzera Italiana, Massimiliano Di Penta University of Sannio, Italy
	15:45 10m		Property-based Testing within ML Projects: an Empirical StudyNIER Paper New Ideas and Emerging Results Track Cindy Wauters Vrije Universiteit Brussel, Coen De Roover Vrije Universiteit Brussel Pre-print
	15:55 15m		Toward Debugging Deep Reinforcement Learning Programs with RLExplorerResearch Track Paper Research Track Rached Bouchoucha Polytechnique Montréal, Ahmed Haj Yahmed École Polytechnique de Montréal, Darshan Patil , Janarthanan Rajendran , Amin Nikanjam École Polytechnique de Montréal, Sarath Chandar Polytechnique Montréal, Foutse Khomh Polytechnique Montréal
	16:10 15m		OPass: Orchestrating TVM's Passes for Lowering Memory Footprints of Computation GraphsResearch Track Paper Research Track Pengbo Nie Shanghai Jiao Tong University, Zihan Wang Shanghai Jiao Tong University, Chengcheng Wan East China Normal University, Ziyi Lin Alibaba Group, He Jiang Dalian University of Technology, Jianjun Zhao Kyushu University, Yuting Chen Shanghai Jiao Tong University
	16:25 15m		RetypeR: Integrated Retrieval-based Automatic Program Repair for Python Type ErrorsResearch Track Paper Research Track Sichong Hao Faculty of Computing, Harbin Institute of Technology, Xianjun Shi , Hongwei Liu Faculty of Computing, Harbin Institute of Technology
	16:40 10m		Automated Detection of Algorithm Debt in Deep Learning Frameworks: An Empirical StudyRegistered Reports Paper Registered Reports Track Emmanuel Iko-Ojo Simon Australian National University, Chirath Hettiarachchi Australian National University, Alex Potanin Australian National University, Hanna Suominen Australian National University, Fatemeh Hendijani Fard University of British Columbia DOI Pre-print