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ICSE 2021
Mon 17 May - Sat 5 June 2021

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed in real-world, open-source software, and the technical debt issues they alleviate. We analyzed 26 projects, consisting of 4.2 MLOC, along with 327 manually examined code patches. The results indicate that developers refactor these systems for a variety of reasons, both specific and tangential to ML, some refactorings correspond to established technical debt categories, while others do not, and code duplication is a major cross-cutting theme that particularly involved ML configuration and model code, which was also the most refactored. We also introduce 14 and 7 new ML-specific refactorings and technical debt categories, respectively, and put forth several recommendations, best practices, and anti-patterns. The results can potentially assist practitioners, tool developers, and educators in facilitating long-term ML system usefulness.

Conference Day
Tue 25 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

19:35 - 20:55
1.5.1. Deep Neural Networks: General IssuesTechnical Track / Journal-First Papers / SEIP - Software Engineering in Practice at Blended Sessions Room 1 +12h
Chair(s): Ignacio PanachUniversidad de Valencia
19:35
20m
Paper
Asset Management in Machine Learning: A SurveySEIP
SEIP - Software Engineering in Practice
Samuel IdowuChalmers | University of Gothenburg, Daniel StrüberRadboud University Nijmegen, Thorsten BergerChalmers | University of Gothenburg
Pre-print
19:55
20m
Paper
An Empirical Study of Refactorings and Technical Debt in Machine Learning SystemsTechnical Track
Technical Track
Yiming TangCity University of New York (CUNY) Graduate Center, Raffi KhatchadourianCUNY Hunter College, Mehdi BagherzadehOakland University, Rhia SinghCity University of New York (CUNY) Macaulay Honors College, Ajani StewartCity University of New York (CUNY) Hunter College, Anita RajaCity University of New York (CUNY) Hunter College
Pre-print Media Attached
20:15
20m
Paper
Logram: Efficient Log Parsing Using n-Gram DictionariesJournal-First
Journal-First Papers
Hetong DaiConcordia University, Heng LiPolytechnique Montréal, Che-Shao ChenConcordia University, Weiyi ShangConcordia University, Tse-Hsun (Peter) ChenConcordia University
DOI Pre-print
20:35
20m
Paper
DeepLocalize: Fault Localization for Deep Neural NetworksTechnical Track
Technical Track
Mohammad WardatDept. of Computer Science, Iowa State University, Wei LeDept. of Computer Science, Iowa State University, Hridesh RajanDept. of Computer Science, Iowa State University
Pre-print Media Attached

Conference Day
Wed 26 May

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

07:35 - 08:55
07:35
20m
Paper
Asset Management in Machine Learning: A SurveySEIP
SEIP - Software Engineering in Practice
Samuel IdowuChalmers | University of Gothenburg, Daniel StrüberRadboud University Nijmegen, Thorsten BergerChalmers | University of Gothenburg
Pre-print
07:55
20m
Paper
An Empirical Study of Refactorings and Technical Debt in Machine Learning SystemsTechnical Track
Technical Track
Yiming TangCity University of New York (CUNY) Graduate Center, Raffi KhatchadourianCUNY Hunter College, Mehdi BagherzadehOakland University, Rhia SinghCity University of New York (CUNY) Macaulay Honors College, Ajani StewartCity University of New York (CUNY) Hunter College, Anita RajaCity University of New York (CUNY) Hunter College
Pre-print Media Attached
08:15
20m
Paper
Logram: Efficient Log Parsing Using n-Gram DictionariesJournal-First
Journal-First Papers
Hetong DaiConcordia University, Heng LiPolytechnique Montréal, Che-Shao ChenConcordia University, Weiyi ShangConcordia University, Tse-Hsun (Peter) ChenConcordia University
DOI Pre-print
08:35
20m
Paper
DeepLocalize: Fault Localization for Deep Neural NetworksTechnical Track
Technical Track
Mohammad WardatDept. of Computer Science, Iowa State University, Wei LeDept. of Computer Science, Iowa State University, Hridesh RajanDept. of Computer Science, Iowa State University
Pre-print Media Attached