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ICSE 2023
Sun 14 - Sat 20 May 2023 Melbourne, Australia
Wed 17 May 2023 13:45 - 14:00 at Meeting Room 106 - Defect analysis Chair(s): Kla Tantithamthavorn

Deep learning techniques have shown promising performance in automated software maintenance tasks associated with bug reports. Currently, all existing studies specifically learn the customized representation of bug reports for a specific downstream task. Despite early success, training multiple models for multiple downstream tasks face three issues: complexity, cost, and compatibility, due to the customization, disparity, uniqueness of these automated approaches. To resolve the above challenges, we propose RepresentThemAll, a pre-trained approach that can learn the universal representation of bug reports and handle multiple downstream tasks. Specifically, RepresentThemAll is a universal bug report framework that is pre-trained with two carefully designed learning objectives: one is the dynamic masked language model and another one is a contrastive learning objective, “find yourself”. We evaluate the performance of RepresentThemAll on four downstream tasks, including duplicate bug report detection, bug report summarization, bug priority prediction, and bug severity prediction. Our experimental results show that RepresentThemAll outperforms all baseline approaches on all considered downstream tasks after well-designed fine-tuning.

Wed 17 May

Displayed time zone: Hobart change

13:45 - 15:15
13:45
15m
Talk
RepresentThemAll: A Universal Learning Representation of Bug Reports
Technical Track
Sen Fang Macau University of Science and Technology, Tao Zhang Macau University of Science and Technology, Youshuai Tan Macau University of Science and Technology, He Jiang Dalian University of Technology, Xin Xia Huawei, Xiaobing Sun Yangzhou University
14:00
15m
Talk
Demystifying Exploitable Bugs in Smart Contracts
Technical Track
Zhuo Zhang Purdue University, Brian Zhang Harrison High School (Tippecanoe), Wen Xu PNM Labs, Zhiqiang Lin The Ohio State University
Pre-print
14:15
15m
Talk
Understanding and Detecting On-the-Fly Configuration BugsDistinguished Paper Award
Technical Track
Teng Wang National University of Defense Technology, Zhouyang Jia National University of Defense Technology, Shanshan Li National University of Defense Technology, Si Zheng National University of Defense Technology, Yue Yu College of Computer, National University of Defense Technology, Changsha 410073, China, Erci Xu National University of Defense Technology, Shaoliang Peng Hunan University, Xiangke Liao National University of Defense Technology
Pre-print
14:30
15m
Talk
Explaining Software Bugs Leveraging Code Structures in Neural Machine Translation
Technical Track
Parvez Mahbub Dalhousie University, Ohiduzzaman Shuvo Dalhousie University, Masud Rahman Dalhousie University
Pre-print Media Attached
14:45
15m
Talk
Scalable Compositional Static Taint Analysis for Sensitive Data Tracing on Industrial Micro-Services
SEIP - Software Engineering in Practice
Zexin Zhong Ant Group; University of Technology Sydney, Jiangchao Liu Ant Group, Diyu Wu Ant Group, Peng Di Ant Group, Yulei Sui University of New South Wales, Sydney, Alex X. Liu Ant Group, John C.S. Lui The Chinese University of Hong Kong
15:00
7m
Talk
Exploring the relationship between performance metrics and cost saving potential of defect prediction models
Journal-First Papers
Steffen Tunkel None, Steffen Herbold University of Passau
15:07
7m
Talk
A Machine and Deep Learning analysis among SonarQube rules, Product, and Process Metrics for Faults Prediction
Journal-First Papers
Francesco Lomio Constructor Institute Schaffhausen, Sergio Moreschini Tampere University, Valentina Lenarduzzi University of Oulu