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Mining Software Repositories
1.
Mining Software Repositories
What to do? And where to get data? Israel Herraiz < [email_address] > Universidad Alfonso X el Sabio June 18 th 2010
2.
3.
4.
5.
1. What is
Mining Software Repositories?
6.
7.
8.
Working Conference since
2008
9.
10.
Also includes any
software artifact
11.
12.
Bug tracking systems
13.
Public communication tools
(mailing lists)
14.
Differences between artifact
and repository #include <stdio.h> int main() { printf(“Hello world”); return 0; } Artifact Source code file hello.c - printf(“Hello world”); + printf(“Hello world”); Author: rms Date: 20100618 04:34 UTC Change: +1 -1 Log: Forgot to add new line hello.c.diff Repository Change to an artifact Meta-information
15.
2. Conferences and
journals of interest
16.
Working conferences of
interest IEEE Int. Working Conf. Source Code Analysis & Manipulation (SCAM) http://www.ieee-scam.org IEEE Int. Working Conf. Mining Software Repositories (MSR) http://msr.uwaterloo.ca Deadlines Accept rate Journal possib. January (Februray for the challenge) April 26% (2007) 38% (2008) 45% (2009) 19% (2008) 31% (2010) JSS SCP EMSE IEEE TSE
17.
Conferences of interest
IEEE Int. Conf. Software Engineering (ICSE) http://www.sbs.co.za/ICSE2010/ IEEE Int. Conf. Software Maintenance (ICSM) http://icsm2010.upt.ro/ Deadlines Accept rate Journal possib. April August September 15% (2008) 12% (2009) 14% (2010) 21% (2007) 26% (2008) 22% (2009) No special issues No special issues Empirical Software Eng. & Measurement (EMSE) http://www.esem-conferences.org/ March ? EMSE
18.
19.
20.
21.
22.
23.
3. Tools for
Mining Software Repositories
24.
25.
CVSAnaly – CVS/SVN/Git
repositories log parser
26.
MLStats – Mailman
and Mboxes parser
27.
28.
4. Datasets for
Mining Software Repositories
29.
30.
31.
32.
Datasheets, databases, scripts,
with information about Eclipse bug reports for several releases
33.
http://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/
34.
35.
Control version repositories,
mailing list archives, bug tracking databases
36.
37.
http://www.flossmetrics.org/
38.
39.
~150,000 projects
40.
Mainly metainformation, obtained
through parsing the web pages of the projects
41.
No low level
or fine grained information
42.
http://flossmole.org
43.
44.
http://promisedata.org/
45.
46.
Effort prediction (18)
47.
General (9)
48.
Model-based SE (7)
49.
Text mining (9)
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