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From Igor Wiese <igor.wi...@gmail.com>
Subject Re: Can you help us Hadoop Community?
Date Tue, 15 Dec 2015 03:07:04 GMT
Hi Zhe! Thanks for your answer.

In fact, we are predicting the "co-change" based on contextual
information collected from issues, commits and developers
communication. Considering the files that i described in the example
("/ipc/Client.java" and
"security/SecurityUtil.java") I collected metrics in each issue and
commit from Client.java to predict when Client.java is prone to change
with SecurityUtil.java.

We are thinking to build a webservice to help newcomers during their
first contributions. Our research group interviewed some newcomers and
they told us that it is difficult to find files to change in a
specific issue. We can recommend files to be checked.

>From the committer perspective, we could help in code review tasks.

What do you think?

Our idea

2015-12-14 22:16 GMT-02:00 Zhe Zhang <zhz@apache.org>:
> Hi Igor,
> It's an interesting direction to study tickets/commits in the Hadoop
> community.
> A research group from Univ. Wisconsin did a similar study on Linux file
> systems and I found it quite insightful:
> http://research.cs.wisc.edu/wind/Publications/fsstudy-tos14.pdf
> For your results, could you elaborate why you picked "co-change" as the
> metric, and how to improve software tools from the "co-change" predictions?
> Thanks,
> Zhe
> On Mon, Dec 14, 2015 at 3:01 PM, Igor Wiese <igor.wiese@gmail.com> wrote:
>> Hi, Hadoop Community.
>> My name is Igor Wiese, phd Student from Brazil. I sent an email a week
>> ago about my research. We received some visit to inspect the results
>> but any feedback was provided.
>> 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 Hadoop 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 950 commits in which a pair of files changed
>> together and could correctly predict 47% commits. These were the most
>> useful information for predicting co-changes of files:
>> - sum of number of lines of code added, modified and removed,
>> - number of words used to describe and discuss the issues,
>> - median value of closeness, a social network measure obtained from
>> issue comments,
>> - median value of constraint, a social network measure obtained from
>> issue comments, and
>> - median value of hierarchy, a social network measure obtained from
>> issue comments.
>> To illustrate, consider the following example from our analysis. For
>> release 0.22, the files "/ipc/Client.java" and
>> "security/SecurityUtil.java" changed together in 3 commits. In another
>> 1 commit, only the first file changed, but not the second. Collecting
>> contextual information for each commit made to first file in the
>> previous release, we were able to predict 2 commits in which both
>> files changed together in release 0.22, and we only issued 1 wrong
>> prediction. For this pair of files, the most important contextual
>> information were the social network metrics (density, hierarchy,
>> efficiency) obtained from issue comments.
>> - 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/70-hadoop
>> All the best,
>> Igor Wiese
>> Phd Candidate
>> --
>> =================================
>> Igor Scaliante Wiese
>> PhD Candidate - Computer Science @ IME/USP
>> Faculty in Dept. of Computing at Universidade Tecnológica Federal do Paraná

Igor Scaliante Wiese
PhD Candidate - Computer Science @ IME/USP
Faculty in Dept. of Computing at Universidade Tecnológica Federal do Paraná

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