MSR 2020 : Mining Software Repositories
in Conferences Posted on July 9, 2019Conference Information
Submission Deadline | Thursday 16 Jan 2020 |
Proceedings indexed by : ![]() |
Conference Dates | May 25, 2020 - May 26, 2020 | |
Conference Address |
Seoul, South Korea ![]() |
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Conference & Submission Link | https://2020.msrconf.org/home |

Conference Ranking & Metrics (This is a TOP Conference)
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Proceedings | https://ieeexplore.ieee.org/xpl/conhome/1001959/all-proceedings |
DBLP Page | https://dblp.uni-trier.de/db/conf/msr |
Conference Call for Papers
The International Conference on Mining Software Repositories (MSR) has hosted a mining challenge since 2006. With this challenge, we call upon everyone interested to apply their tools to a common dataset. The challenge is for researchers and practitioners to bravely use their mining tools and approaches on a dare. Right now, there is a call for challenge proposals. See the CFP for more details. The current important dates are:
One of the secret ingredients behind the success of the International Conference on Mining Software Repositories (MSR) is its annual Mining Challenge, in which MSR participants can showcase their techniques, tools and creativity on a common data set. In true MSR fashion, this data set is a “real” data set contributed by researchers in the community, solicited through an open call.
There are many benefits of sharing a data set for the MSR Mining Challenge. The selected challenge proposal explaining the data set will appear in the MSR 2020 proceedings, and the challenge papers using the data set will be required to cite the challenge proposal or an existing paper of the researchers about the selected data set. Furthermore, the authors of the data set will become the official 2020 Mining Challenge Chairs, responsible for the reviewing process (e.g., composing a Challenge PC, managing the submissions and review assignments, etc.). Finally, it is not uncommon for challenge data sets to be featured in MSR papers and other conference publications well after the conference is finished!
If you would like to compete for a chance to have your data set featured in the 2020 MSR Mining Challenge and all the benefits that come with it, please submit a 1-page proposal with up to 3 pages of appendix containing the following information:
Title of data set.
What does your data set contain?
How large is it?
How accessible is it and how can the data be obtained?
How representative is it?
Does it require specialized tools to process it?
What would challenge participants need to work with the data set?
What kind of questions do you expect challenge participants to answer?
A link to a small sample of the data (e.g., via dropbox, github, etc.).
The above submissions should use the ACM template (https://www.acm.org/publications/proceedings-template for both LaTeX and Word users). LaTeX users must use the provided acmart.clsand ACM-Reference-Format.bst without modification, enable the conference format in the preamble of the document (i.e., \\documentclass[sigconf,review]{acmart}), and use the ACM reference format for the bibliography (i.e., \\bibliographystyle{ACM-Reference-Format}). The review option adds line numbers, thereby allowing referees to refer to specific lines in their comments. Submissions will be examined and discussed by the MSR 2020 PC chairs, General Chair, and potentially steering committee members in case of conflicts of interest.
Once the winning proposal is selected, its authors will become the MSR 2020 Challenge Chairs and will be responsible for choosing the MSR 2020 Mining Challenge program committee. The major deadline for having all material related to the selected challenge available on the MSR 2020 website is Friday August 30th 2019. At that time, the Challenge CFP will be announced and the full challenge data set will be publicly released. Two more weeks, until Friday September 13th, will be allowed to find the Challenge PC members. By making the challenge data set available before the Fall term starts, we hope that many student teams will be able to use the challenge data set for their graduate class projects.
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