2021 IEEE/ACM 18th International Conference on Mining Software Repositories MSR

活动简介

The Mining Software Repositories (MSR) conference is the premier conference for data science, machine learning, and artificial intelligence in software engineering. The goal of the conference is to improve software engineering practices by uncovering interesting and actionable information about software systems and projects using the vast amounts of software data such as source control systems, defect tracking systems, code review repositories, archived communications between project personnel, question-and-answer sites, CI build servers, and run-time telemetry. Mining this information can help to understand software development and evolution, software users, and runtime behavior; support the maintenance of software systems; improve software design/reuse; empirically validate novel ideas and techniques; support predictions about software development; and exploit this knowledge in planning future development.

Sponsor Type:1; 9

组委会

Gregorio Robles, General Chair
Universidad Rey Juan Carlos
Spain

Kelly Blincoe, Program Co-Chair
University of Auckland
New Zealand

Mei Nagappan, Program Co-Chair
University of Waterloo
Canada

Miltiadis Allamanis, Mining Challenge Co-Chair
Microsoft Research, UK
United Kingdom

Rafael-Michael Karampatsis, Mining Challenge Co-Chair
The University of Edinburgh
United Kingdom

Charles Sutton, Mining Challenge Co-Chair
Google Research
United States

Marco D'ambros, Tutorials Co-Chair

Minghui Zhou, Tutorials Co-Chair
Peking University, China
China

Tegawendé F. Bissyandé, Registered Reports Co-chair
SnT, University of Luxembourg

David Lo Registered, Reports Co-chair
Singapore Management University

Mario Linares-Vasquez, Data Showcase Co-Chair
Universidad de los Andes
Colombia

Baishakhi Ray, Data Showcase Co-Chair
Columbia University, USA
United States

Jim Herbsleb, Hackathon Track Co-chair
Carnegie Mellon University
United States

Audris Mockus, Hackathon Track Co-chair
The University of Tennessee

Alexander Nolte, Hackathon Track Co-chair
University of Tartu
Estonia

Gema Rodriguez, Perez Proceedings Chair
University of Waterloo
Canada

Sridhar Chimalakonda, Publicity Co-Chair
Indian Institute of Technology Tirupati
India
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Jürgen Cito, Publicity Co-Chair
TU Wien and MIT
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Raula Gaikovina Kula, Social Media Co-Chair
Nara Institute of Science and Technology

Maleknaz Nayebi, Social Media Co-Chair
Polytechnique Montréal
Canada

Amjed Tahir, Web Chair
Massey University
New Zealand

Serge Demeyer, Shadow PC Advisor
University of Antwerp, Belgium
Belgium

Foutse Khomh, Shadow PC Advisor
Polytechnique Montréal
Canada

Ayushi Rastogi, Shadow PC Co-Chair

Patanamon Thongtanunam, Shadow PC Co-Chair
The University of Melbourne
Australia

Shane McIntosh, Diversity and Inclusion
University of Waterloo
Canada

Gustavo Pinto, Diversity and Inclusion
Federal University of Pará

征稿信息

重要日期

2021-01-05

摘要截稿日期

2021-01-12

初稿截稿日期

征稿范围

The technical track of MSR 2021 solicits high-quality submissions on a wide range of topics related to artificial intelligence (AI), machine learning (ML), and data science (DS) in one or more of the following three main themes.

1. AI/ML/DS and SE

The analysis should aim to improve understanding of development processes and practices or aid in the development of new techniques or models to support software developers. This includes (but is not limited to) analysis or models for:

commits,
execution traces and logs,
interaction data,
code review data,
natural language artifacts,
software licenses and copyrights,
app store data,
programming language features,
release information,
CI logs,
deployment and delivery,
test data,
runtime information,
software ecosystems,
defect and software quality data,
human and social aspects of development,
development process,
energy profile data.

2. New techniques, tools, and models

The techniques, tools, and models should facilitate new ways to mine, analyze, or model software data. A submission could include (but is not limited to) techniques, tools, or models to:

capture new forms of data,
integrate data from multiple sources,
visualize software data,
model software data,
solve SE problems,
improve AI/ML/DS.

3. Considerations related to AI/ML/DS and SE

These submissions should reflect on the current state-of-the-art research methods or current practices in mining, analyzing, or modeling software data. These submissions can also propose new research methods or guidelines. This theme includes topics such as (but not limited to)：

privacy of collected data,
ethics of mining, analyzing, or modelling software data,
biases in software data, analyses, and tools,
fairness in software data, analyses, and tools,
Replication studies.