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ICSE 2022
Sun 8 - Fri 27 May 2022
Tue 10 May 2022 21:05 - 21:10 at ICSE room 3-odd hours - Machine Learning with and for SE 6 Chair(s): Ali Ouni
Wed 11 May 2022 12:15 - 12:20 at ICSE room 1-even hours - Machine Learning with and for SE 11 Chair(s): Ipek Ozkaya

Automatically localizing software bugs to the changesets that induced them has the potential to improve software developer efficiency and to positively affect software quality. To facilitate this automation, a bug report has to be effectively matched with source code changes, even when a significant lexical gap exists between natural language used to describe the bug and identifier naming practices used by developers. To bridge this gap, we need techniques that are able to capture software engineering-specific and project-specific semantics in order to detect relatedness between the two types of documents that goes beyond exact term matching. Popular transformer-based deep learning architectures, such as BERT, excel at leveraging contextual information, hence appear to be a suitable candidate for the task. However, BERT-like models are computationally expensive, which precludes them from being used in an environment where response time is important.

In this paper, we describe how BERT can be made fast enough to be applicable to changeset-based bug localization. We also explore several design decisions in using BERT for this purpose, including how best to encode changesets and how to match bug reports to individual changes for improved accuracy. We compare the accuracy and performance of our model to a non-contextual baseline (i.e., vector space model) and BERT-based architectures previously used in software engineering. Our evaluation results demonstrate advantages in using the proposed BERT model compared to the baselines, especially for bug reports that lack any hints about related code elements.

Tue 10 May

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

21:00 - 22:00
Machine Learning with and for SE 6Technical Track at ICSE room 3-odd hours
Chair(s): Ali Ouni ETS Montreal, University of Quebec
21:00
5m
Talk
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
Technical Track
Jialun Cao Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Meiziniu LI Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Xiao Chen Huazhong University of Science and Technology, Ming Wen Huazhong University of Science and Technology, Yongqiang Tian University of Waterloo, Bo Wu MIT-IBM Watson AI Lab in Cambridge, Shing-Chi Cheung Hong Kong University of Science and Technology
DOI Pre-print Media Attached
21:05
5m
Talk
Fast Changeset-based Bug Localization with BERT
Technical Track
Agnieszka Ciborowska Virginia Commonwealth University, Kostadin Damevski Virginia Commonwealth University
Pre-print Media Attached
21:10
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
21:15
5m
Talk
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
Technical Track
haibin zheng Zhejiang University of Technology, Zhiqing Chen Zhejiang University of Technology, Tianyu Du Zhejiang University, Xuhong Zhang Zhejiang University, Yao Cheng Huawei International, Shouling Ji Zhejiang University, Jingyi Wang Zhejiang University, Yue Yu College of Computer, National University of Defense Technology, Changsha 410073, China, Jinyin Chen College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
DOI Pre-print Media Attached
21:20
5m
Talk
Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python
Technical Track
Amir Mir Delft University of Technology, Evaldas Latoskinas Delft University of Technology, Sebastian Proksch Delft University of Technology, Netherlands, Georgios Gousios Endor Labs & Delft University of Technology
DOI Pre-print Media Attached
21:25
5m
Talk
Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules
Technical Track
Rangeet Pan Iowa State University, USA, Hridesh Rajan Iowa State University
Pre-print Media Attached

Wed 11 May

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

12:00 - 13:00
Machine Learning with and for SE 11Journal-First Papers / Technical Track at ICSE room 1-even hours
Chair(s): Ipek Ozkaya Carnegie Mellon Software Engineering Institute
12:00
5m
Talk
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
Journal-First Papers
Nadia Daoudi 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
12:05
5m
Talk
DeepAnalyze: Learning to Localize Crashes at Scale
Technical Track
Manish Shetty Microsoft Research, India, Chetan Bansal Microsoft Research, Suman Nath Microsoft Corporation, Sean Bowles Microsoft, Henry Wang Microsoft, Ozgur Arman Microsoft, Siamak Ahari Microsoft
Pre-print Media Attached
12:10
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
12:15
5m
Talk
Fast Changeset-based Bug Localization with BERT
Technical Track
Agnieszka Ciborowska Virginia Commonwealth University, Kostadin Damevski Virginia Commonwealth University
Pre-print Media Attached
12:20
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
12:25
5m
Talk
Using Pre-Trained Models to Boost Code Review Automation
Technical Track
Rosalia Tufano Università della Svizzera Italiana, Simone Masiero Software Institute @ Università della Svizzera Italiana, Antonio Mastropaolo Università della Svizzera italiana, Luca Pascarella Università della Svizzera italiana (USI), Denys Poshyvanyk William and Mary, Gabriele Bavota Software Institute, USI Università della Svizzera italiana
Pre-print Media Attached
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Information for Participants
Tue 10 May 2022 21:00 - 22:00 at ICSE room 3-odd hours - Machine Learning with and for SE 6 Chair(s): Ali Ouni
Info for room ICSE room 3-odd hours:

Click here to go to the room on Midspace

Wed 11 May 2022 12:00 - 13:00 at ICSE room 1-even hours - Machine Learning with and for SE 11 Chair(s): Ipek Ozkaya
Info for room ICSE room 1-even hours:

Click here to go to the room on Midspace