Write a Blog >>
ICSE 2020
Wed 24 June - Thu 16 July 2020
Wed 8 Jul 2020 00:00 - 00:12 at Silla - P9-Bugs and Repair Chair(s): Yingfei Xiong

Patch recommendation is the process of identifying errors in software systems and suggesting suitable fixes for them. Patch recommendation can significantly improve developer productivity by reducing both the debugging and repairing time. Existing techniques usually rely on complete test suites and detailed debugging reports, which are often absent in practical industrial settings. In this paper, we propose PRECFIX, a pragmatic approach targeting large-scale industrial codebase and making recommendations based on previously observed debugging activities. PRECFIX collects defect-patch pairs from development histories, performs clustering, and extracts generic reusable patching patterns as recommendations. We conducted experimental study on an industrial codebase with 10K projects involving diverse defect patterns. We managed to extract 3K templates of defect-patch pairs, which have been successfully applied to the entire codebase. Our approach is able to make recommendations within milliseconds and achieves a false positive rate of 22% confirmed by manual review. The majority (10/12) of the interviewed developers appreciated PRECFIX, which has been rolled out to Alibaba to support various critical businesses.

Wed 8 Jul

Displayed time zone: (UTC) Coordinated Universal Time change

00:00 - 01:00
00:00
12m
Talk
PRECFIX: Large-Scale Patch Recommendation by Mining Defect-Patch PairsSEIP
Software Engineering in Practice
Xindong Zhang Alibaba Group, Chenguang Zhu University of Texas, Austin, Yi Li Nanyang Technological University, Jianmei Guo Alibaba Group, Lihua Liu Alibaba Group, Haobo Gu Alibaba Group
Pre-print
00:12
12m
Talk
On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java ProgramsArtifact ReusableTechnicalArtifact Available
Technical Papers
Kui Liu Nanjing University of Aeronautics and Astronautics, Shangwen Wang National University of Defense Technology, Anil Koyuncu University of Luxembourg, Luxembourg, Kisub Kim University of Luxembourg, SnT, Tegawendé F. Bissyandé SnT, University of Luxembourg, Dongsun Kim Furiosa.ai, Peng Wu National University of Defense Technology, Jacques Klein University of Luxembourg, SnT, Xiaoguang Mao National University of Defense Technology, Yves Le Traon University of Luxembourg
Pre-print
00:24
8m
Talk
SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program RepairJ1
Journal First
Zimin Chen KTH Royal Institute of Technology, Steve Kommrusch Colorado State University, Michele Tufano College of William and Mary, Louis-Noël Pouchet Colorado State University, USA, Denys Poshyvanyk William and Mary, Martin Monperrus KTH Royal Institute of Technology
00:32
8m
Talk
A Study of Bug Resolution Characteristics in Popular Programming LanguagesJ1
Journal First
Jie M. Zhang University College London, UK, Feng Li , Dan Hao Peking University, Meng Wang University of Bristol, UK, Hao Tang Peking University, Lu Zhang Peking University, China, Mark Harman Facebook and University College London
00:40
12m
Talk
Automated Bug Reproduction from User Reviews for Android ApplicationsSEIP
Software Engineering in Practice
Shuyue Li Xi'an Jiaotong University, Jiaqi Guo Xi'an Jiaotong University, Ming Fan Xi'an Jiaotong University, Jian-Guang Lou Microsoft Research, Qinghua Zheng Xi'an Jiaotong University, Ting Liu Xi'an Jiaotong University
00:52
6m
Talk
CHASE: Checklist to Assess User Experience in Internet of Things EnvironmentsNIER
New Ideas and Emerging Results
Rodrigo Almeida Federal University of Ceará, Joseane Paiva Federal University of Ceará, Rossana Andrade Federal University of Ceará, Ticianne Darin Federal University of Ceará