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From igorwiese <igor.wi...@gmail.com>
Subject Feedback of my Phd work in Camel Project
Date Wed, 09 Dec 2015 23:19:08 GMT
Hi, Camel Community. 

My name is Igor Wiese, phd Student from Brazil. I am investigating two
important questions: What makes two files change together? Can we predict
when they are going to co-change again? 

I've tried to investigate this question on the Camel project. I've collected
data from issue reports, discussions and commits and using some machine
learning techniques to build a prediction model.

I collected a total of 1543 commits in which a pair of files changed
together and could correctly predict 79% commits. These were the most useful
information for predicting co-changes of files: 
- number of lines of code added, 
- number of lines of code removed, 
- sum of number of lines of code added, modified and removed, 
- number of words used to describe and discuss the issues, and 
- number of comments in each issue.

To illustrate, consider the following example from our analysis. For release
2.1, the files "component/jms/JmsComponent.java" and
"component/jms/JmsConfiguration.java" changed together in 10 commits. In
another 5 commits, only the first file changed, but not the second.
Collecting contextual information for each commit made to first file in
release 2.0, we were able to predict all 10 commits in which both files
changed together in release 2.1, and we only issued one false positive. For
this pair of files, the most important contextual information was the number
of lines of code added in each commit and the number of words used to
describe and discuss the issues.

- Do these results surprise you? Can you think in any explanation for the
results?
- Do you think that our rate of prediction is good enough to be used for
building tool support for the software community?
- Do you have any suggestion on what can be done to improve the change
recommendation?

You can visit our webpage to inspect the results in details:
http://flosscoach.com/index.php/17-cochanges/65-camel

All the best, 
Igor Wiese
Phd Candidate



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