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ICSE 2022
Sun 8 - Fri 27 May 2022

Variable names are critical for conveying intended program behavior. Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection. Ideally, such methods could capture semantic relationships between names beyond syntactic similarity, e.g., the fact that the names average and mean are similar. Unfortunately, previous work has found that even the best of previous representation approaches primarily capture “relatedness” (whether two variables are linked at all), rather than “similarity” (whether they actually have the same meaning).

We propose VarCLR, a new approach for learning semantic representations of variable names that effectively captures variable similarity in this stricter sense. We observe that this problem is an excellent fit for contrastive learning, which aims to minimize the distance between explicitly similar inputs, while maximizing the distance between dissimilar inputs. This requires labeled training data, and thus we construct a novel, weakly-supervised variable renaming dataset mined from GitHub edits. We show that VarCLR enables the effective application of sophisticated, general-purpose language models like BERT, to variable name representation and thus also to related downstream tasks like variable name similarity search or spelling correction. VarCLR produces models that significantly outperform the state-of-the-art on IdBench, an existing benchmark that explicitly captures variable similarity (as distinct from relatedness). Finally, we contribute a release of all data, code, and pre-trained models, aiming to provide a plug-in replacement for variable representations used in either existing or future program analyses that rely on variable names.

Mon 9 May

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

22:00 - 23:00
Machine Learning with and for SE 5Technical Track / Journal-First Papers / SEIP - Software Engineering in Practice at ICSE room 1-even hours
Chair(s): Jürgen Cito TU Wien and Meta
22:00
5m
Talk
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations
Journal-First Papers
Amin Nikanjam École Polytechnique de Montréal, Houssem Ben Braiek École Polytechnique de Montréal, Mohammad Mehdi Morovati École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal
Link to publication DOI Media Attached
22:05
5m
Talk
Counterfactual Explanations for Models of Code
SEIP - Software Engineering in Practice
Jürgen Cito TU Wien and Meta, Işıl Dillig University of Texas at Austin, Vijayaraghavan Murali Meta Platforms, Inc., Satish Chandra Facebook
Pre-print Media Attached
22:10
5m
Talk
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Technical Track
Qibin Chen Carnegie Mellon University, Jeremy Lacomis Carnegie Mellon University, Edward J. Schwartz Carnegie Mellon University Software Engineering Institute, Graham Neubig Carnegie Mellon University, Bogdan Vasilescu Carnegie Mellon University, USA, Claire Le Goues Carnegie Mellon University
DOI Pre-print Media Attached
22:15
5m
Talk
Towards Training Reproducible Deep Learning Models
Technical Track
Boyuan Chen Centre for Software Excellence, Huawei Canada, Mingzhi Wen Huawei Technologies, Yong Shi Huawei Technologies, Dayi Lin Centre for Software Excellence, Huawei, Canada, Gopi Krishnan Rajbahadur Centre for Software Excellence, Huawei, Canada, Zhen Ming (Jack) Jiang York University
Pre-print Media Attached
22:20
5m
Talk
Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and ProcessDistinguished Paper Award
Technical Track
Nadia Nahar Carnegie Mellon University, Shurui Zhou University of Toronto, Grace Lewis Carnegie Mellon Software Engineering Institute, Christian Kästner Carnegie Mellon University
Pre-print Media Attached
22:25
5m
Talk
Detecting False Alarms from Automatic Static Analysis Tools: How Far are We?Nominated for Distinguished Paper
Technical Track
Hong Jin Kang Singapore Management University, Khai Loong Aw Singapore Management University, David Lo Singapore Management University
DOI Pre-print Media Attached File Attached

Wed 11 May

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

11:00 - 12:00
Machine Learning with and for SE 10Technical Track / SEIP - Software Engineering in Practice / Journal-First Papers at ICSE room 1-odd hours
Chair(s): Preetha Chatterjee Drexel University, USA
11:00
5m
Talk
Defect Reduction Planning (using TimeLIME)
Journal-First Papers
Kewen Peng North Carolina State University, Tim Menzies North Carolina State University
Authorizer link Pre-print Media Attached
11:05
5m
Talk
Automatic Fault Detection for Deep Learning Programs Using Graph Transformations
Journal-First Papers
Amin Nikanjam École Polytechnique de Montréal, Houssem Ben Braiek École Polytechnique de Montréal, Mohammad Mehdi Morovati École Polytechnique de Montréal, Foutse Khomh Polytechnique Montréal
Link to publication DOI Media Attached
11:10
5m
Talk
Counterfactual Explanations for Models of Code
SEIP - Software Engineering in Practice
Jürgen Cito TU Wien and Meta, Işıl Dillig University of Texas at Austin, Vijayaraghavan Murali Meta Platforms, Inc., Satish Chandra Facebook
Pre-print Media Attached
11:15
5m
Talk
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Technical Track
Qibin Chen Carnegie Mellon University, Jeremy Lacomis Carnegie Mellon University, Edward J. Schwartz Carnegie Mellon University Software Engineering Institute, Graham Neubig Carnegie Mellon University, Bogdan Vasilescu Carnegie Mellon University, USA, Claire Le Goues Carnegie Mellon University
DOI Pre-print Media Attached
11:20
5m
Talk
Towards Training Reproducible Deep Learning Models
Technical Track
Boyuan Chen Centre for Software Excellence, Huawei Canada, Mingzhi Wen Huawei Technologies, Yong Shi Huawei Technologies, Dayi Lin Centre for Software Excellence, Huawei, Canada, Gopi Krishnan Rajbahadur Centre for Software Excellence, Huawei, Canada, Zhen Ming (Jack) Jiang York University
Pre-print Media Attached
11:25
5m
Talk
Learning to Reduce False Positives in Analytic Bug Detectors
Technical Track
Anant Kharkar Microsoft, Roshanak Zilouchian Moghaddam Microsoft, Matthew Jin Microsoft Corporation, Xiaoyu Liu Microsoft Corporation, Xin Shi Microsoft Corporation, Colin Clement Microsoft, Neel Sundaresan Microsoft Corporation
Pre-print Media Attached

Fri 27 May

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

11:00 - 12:30
Papers 19: Machine Learning with and for SE 2Journal-First Papers / Technical Track at Room 301+302
Chair(s): Dalal Alrajeh Imperial College London
11:00
5m
Talk
Defect Reduction Planning (using TimeLIME)
Journal-First Papers
Kewen Peng North Carolina State University, Tim Menzies North Carolina State University
Authorizer link Pre-print Media Attached
11:05
5m
Talk
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Technical Track
Qibin Chen Carnegie Mellon University, Jeremy Lacomis Carnegie Mellon University, Edward J. Schwartz Carnegie Mellon University Software Engineering Institute, Graham Neubig Carnegie Mellon University, Bogdan Vasilescu Carnegie Mellon University, USA, Claire Le Goues Carnegie Mellon University
DOI Pre-print Media Attached
11:10
5m
Talk
EREBA: Black-box Energy Testing of Adaptive Neural Networks
Technical Track
Mirazul Haque UT Dallas, Yaswanth Yadlapalli University of Texas at Dallas, Wei Yang University of Texas at Dallas, Cong Liu University of Texas at Dallas, USA
Pre-print Media Attached
11:15
5m
Talk
Multilingual training for Software Engineering
Technical Track
Toufique Ahmed University of California at Davis, Prem Devanbu Department of Computer Science, University of California, Davis
DOI Pre-print Media Attached
11:20
5m
Talk
Learning to Recognize Actionable Static Code Warnings (is Intrinsically Easy)
Journal-First Papers
Xueqi Yang NCSU, Jianfeng Chen North Carolina State University, Rahul Yedida North Carolina State University, Zhe Yu , Tim Menzies North Carolina State University
Link to publication DOI Pre-print Media Attached
11:25
5m
Talk
Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and ProcessDistinguished Paper Award
Technical Track
Nadia Nahar Carnegie Mellon University, Shurui Zhou University of Toronto, Grace Lewis Carnegie Mellon Software Engineering Institute, Christian Kästner Carnegie Mellon University
Pre-print Media Attached
11:30
5m
Talk
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
Journal-First Papers
Nadia Daoudi SnT, University of Luxembourg, Kevin Allix University of Luxembourg, Tegawendé F. Bissyandé SnT, University of Luxembourg, Jacques Klein University of Luxembourg
Link to publication Pre-print Media Attached

Information for Participants
Mon 9 May 2022 22:00 - 23:00 at ICSE room 1-even hours - Machine Learning with and for SE 5 Chair(s): Jürgen Cito
Info for room ICSE room 1-even hours:

Click here to go to the room on Midspace

Wed 11 May 2022 11:00 - 12:00 at ICSE room 1-odd hours - Machine Learning with and for SE 10 Chair(s): Preetha Chatterjee
Info for room ICSE room 1-odd hours:

Click here to go to the room on Midspace