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ESEC/FSE 2022
Mon 14 - Fri 18 November 2022 Singapore
Tue 15 Nov 2022 14:30 - 14:45 at SRC Auditorium 2 - Machine Learning III

As User Reviews (URs) of mobile Apps are proven to provide valuable feedback for maintaining and evolving mobile applications, how to make full use of URs more efficiently in the release cycle of mobile Apps has become a widely concerned and researched topic in the Software Engineering (SE) community. In order to speed up the completion of coding work related to URs to shorten the release cycle as much as possible, the task of User Review-based code localization is proposed and studied in depth. However, due to the lack of large-scale ground truth dataset (i.e., truly related <UR,Code> pairs), existing methods are all unsupervised learning-based. In order to light up supervised learning approaches, which are driven by large labeled datasets, for Review2Code, and to compare their performances with unsupervised learning-based methods, in this paper, we first introduce a newly constructed large-scale human-labeled <UR,Code> ground truth dataset, including the annotation process and statistical analysis. Then, a benchmark consisting of two SOTA unsupervised learning-based and four supervised learning-based Review2Code methods is constructed based on this dataset. We believe that this paper can provide a basis for in-depth exploration of the supervised learning-based Review2Code solutions.

Tue 15 Nov

Displayed time zone: Beijing, Chongqing, Hong Kong, Urumqi change

14:00 - 15:30
14:00
15m
Talk
AutoPruner: Tranformer-based Call Graph Pruning
Research Papers
Le-Cong Thanh Singapore Management University, Singapore, Hong Jin Kang Singapore Management University, Singapore, Truong Giang Nguyen School of Computing and Information Systems, Singapore Management University, Stefanus Agus Haryono Singapore Management University, David Lo Singapore Management University, Xuan-Bach D. Le Singapore Management University, Singapore, Huynh Quyet Thang Hanoi University of Science and Technology
Pre-print
14:15
15m
Talk
Exploring and Evaluating Personalized Models for Code Generation
Industry Paper
Andrei Zlotchevski McGill University, Dawn Drain , Alexey Svyatkovskiy Microsoft, Colin Clement Microsoft, Neel Sundaresan Microsoft Corporation, Michele Tufano Microsoft
DOI Pre-print
14:30
15m
Talk
Lighting Up Supervised Learning in User Review-Based Code Localization: Dataset and Benchmark
Research Papers
Xinwen Hu State Key Laboratory for Novel Software and Technology, Nanjing University, 22 Hankou Road, Nanjing, China, Yu Guo State Key Laboratory for Novel Software and Technology, Nanjing University, 22 Hankou Road, Nanjing, China, Jianjie Lu State Key Laboratory for Novel Software and Technology, Nanjing University, 22 Hankou Road, Nanjing, China, Zheling Zhu State Key Laboratory for Novel Software and Technology, Nanjing University, 22 Hankou Road, Nanjing, China, Chuanyi Li State Key Laboratory for Novel Software Technology, Nanjing University, Jidong Ge , Liguo Huang Dept. of Computer Science, Southern Methodist University, Dallas, TX, 75205, Bin Luo Software Institute, Nanjing University
14:45
15m
Talk
CORMS: A GitHub and Gerrit based Hybrid Code Reviewer Recommendation Approach for Modern Code Review
Research Papers
Pandya Prahar Hemantkumar DAIICT Gandhinagar, India, Saurabh Tiwari DAIICT Gandhinagar, India
15:00
15m
Full-paper
Hierarchical Multi-Kernel Relevant Vector Machines for Requirement Extraction from App Reviews
Research Papers
Moayad Alshangiti University of Jeddah, Weishi Shi Rochester Institute of Technology, Eduardo Coelho de Lima Rochester Institute of Technology, Xumin Liu Rochester Institute of Technology, Qi Yu RIT
15:15
15m
Talk
Semi-Supervised Pre-processing for Learning-based Traceability Framework on Real-World Software Projects
Research Papers
Liming Dong Nanjing University, He Zhang Nanjing University, Wei Liu Nanjing University, Zhiluo Weng Nanjing University, Hongyu Kuang Nanjing University