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From Igor Wiese <>
Subject Feedback of my Phd work in Hive Project
Date Wed, 09 Dec 2015 23:39:07 GMT
Hi, Hive Community.

My name is Igor Wiese, phd Student from Brazil. In my research 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 Hive 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 721 commits in which a pair of files changed
together and could correctly predict 53% 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,

- number of comments in each issue,

- median value of closeness, a social network measure obtained from issue
comments, and

- median value of effective size, a social network measure obtained from
issue comments.

To illustrate, consider the following example from our analysis. For
release 0.14, the files "metastore/" and
"metastore/" changed together in 4 commits. In another 2
commits, only the first file changed, but not the second. Collecting
contextual information for each commit made to first file in previous
release, we were able to predict 4 commits in which both files changed
together in release 0.14, and we issued 0 false positives and two wrong
predictions. For this pair of files, the most important contextual
information was the number of lines of codes added, the sum of lines of
codes added, removed and modified, and two social network metrics
(constraint, ties) obtained from issue comments

- Do these results surprise you? Can you think in any explanation for the

- 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

You can visit a webpage to inspect the results in details:

All the best,
Igor Wiese
Phd Candidate

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