A Study of Bug Resolution Characteristics in Popular Programming LanguagesJ1
This paper presents a large-scale study that investigates the bug resolution characteristics among popular Github projects written in different programming languages. We explore correlations but, of course, we cannot infer causation. Specifically, we analyse bug resolution data from approximately 70 million Source Line of Code, drawn from 3 million commits to 600 GitHub projects, primarily written in 10 programming languages. We find notable variations in apparent bug resolution time and patch (fix) size. While interpretation of results from such large-scale empirical studies is inherently difficult, we believe that the differences in medians are sufficiently large to warrant further investigation, replication, re-analysis and follow up research. For example, in our corpus, the median apparent bug resolution time (elapsed time from raise to resolve) for Ruby was 4X that for Go and 2.5X for Java. We also found that patches tend to touch more files for the corpus of strongly typed and for statically typed programs. However, we also found evidence for a lower elapsed resolution time for bug resolution committed to projects constructed from statically typed languages. These findings, if replicated in subsequent follow on studies, may shed further empirical light on the debate about the importance of static typing.
Wed 8 JulDisplayed time zone: (UTC) Coordinated Universal Time change
00:00 - 01:00 | P9-Bugs and RepairJournal First / Technical Papers / Software Engineering in Practice / New Ideas and Emerging Results at Silla Chair(s): Yingfei Xiong Peking University, China | ||
00:00 12mTalk | 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 12mTalk | On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java ProgramsTechnical Technical Papers Kui Liu Huawei Software Engineering Application Technology Lab, 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 8mTalk | 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 8mTalk | 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 12mTalk | 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 6mTalk | 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 |