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ESEC/FSE 2021
Thu 19 - Sat 28 August 2021 Clowdr Platform

Modern code review (MCR) processes are prevalent in most
organizations that develop software due to benefits in quality
assurance and knowledge transfer. With the rise of collaborative
software development platforms like GitHub and
Bitbucket, today, millions of projects share not only their
code but also their review data. Although researchers have
tried to exploit this data for more than a decade, most of
that knowledge remains a buried treasure. A crucial catalyst
for many advances in deep learning, however, is the accessibility
of large-scale standard datasets for different learning
tasks. This paper presents the ETCR (Exploit Those Code
Reviews!) infrastructure for mining MCR datasets from any
GitHub project practicing pull-request-based development.
We demonstrate its effectiveness with ETCR-Elasticsearch,
a dataset of >231𝑘 review comments for >47𝑘 Java file revisions
in >40𝑘 pull-requests from the Elasticsearch project.
ETCR is designed with the challenge of deep learning in
mind. Compared to previous datasets, ETCR datasets include
all information for linking review comments to nodes
in the respective program’s Abstract Syntax Tree.

Wed 25 Aug

Displayed time zone: Athens change

09:00 - 10:00
Analytics & Software Evolution—Code Reviews and ChangesJournal First / Research Papers / Demonstrations / Ideas, Visions and Reflections +12h
Chair(s): Ingrid Nunes Universidade Federal do Rio Grande do Sul (UFRGS), Brazil, Anthony Cleve University of Namur
09:00
10m
Paper
Identifying Bad Software Changes via Multimodal Anomaly Detection for Online Service Systems
Research Papers
Nengwen Zhao Tsinghua University, Junjie Chen Tianjin University, Zhaoyang Yu Tsinghua University, Honglin Wang BizSeer, Jiesong Li China Guangfa Bank, Bin Qiu China Guangfa Bank, Hongyu Xu China Guangfa Bank, Wenchi Zhang BizSeer, Kaixin Sui BizSeer, Dan Pei Tsinghua University
DOI
09:10
10m
Paper
Journal First Submission of the Article: "An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review"
Journal First
Sebastiano Panichella Zurich University of Applied Sciences, Nick Zaugg University of Zurich
09:20
5m
Paper
Exploit Those Code Reviews! Bigger Data for Deeper Learning
Demonstrations
Robert Heumüller University of Magdeburg, Sebastian Nielebock Otto-von-Guericke University Magdeburg, Frank Ortmeier University of Magdeburg
DOI Media Attached
09:25
5m
Paper
Towards Automating Code Review at Scale
Ideas, Visions and Reflections
Vincent J. Hellendoorn Carnegie Mellon University, Jason Tsay IBM Research, Manisha Mukherjee Carnegie Mellon University, Martin Hirzel IBM Research
DOI
09:30
30m
Live Q&A
Q&A (Analytics & Software Evolution—Code Reviews and Changes)
Research Papers

21:00 - 22:00
Analytics & Software Evolution—Code Reviews and ChangesResearch Papers / Demonstrations / Ideas, Visions and Reflections / Journal First
Chair(s): Emad Aghajani Software Institute, USI Università della Svizzera italiana
21:00
10m
Paper
Identifying Bad Software Changes via Multimodal Anomaly Detection for Online Service Systems
Research Papers
Nengwen Zhao Tsinghua University, Junjie Chen Tianjin University, Zhaoyang Yu Tsinghua University, Honglin Wang BizSeer, Jiesong Li China Guangfa Bank, Bin Qiu China Guangfa Bank, Hongyu Xu China Guangfa Bank, Wenchi Zhang BizSeer, Kaixin Sui BizSeer, Dan Pei Tsinghua University
DOI
21:10
10m
Paper
Journal First Submission of the Article: "An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review"
Journal First
Sebastiano Panichella Zurich University of Applied Sciences, Nick Zaugg University of Zurich
21:20
5m
Paper
Exploit Those Code Reviews! Bigger Data for Deeper Learning
Demonstrations
Robert Heumüller University of Magdeburg, Sebastian Nielebock Otto-von-Guericke University Magdeburg, Frank Ortmeier University of Magdeburg
DOI Media Attached
21:25
5m
Paper
Towards Automating Code Review at Scale
Ideas, Visions and Reflections
Vincent J. Hellendoorn Carnegie Mellon University, Jason Tsay IBM Research, Manisha Mukherjee Carnegie Mellon University, Martin Hirzel IBM Research
DOI
21:30
30m
Live Q&A
Q&A (Analytics & Software Evolution—Code Reviews and Changes)
Research Papers