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From Anshul Gangwar <>
Subject Re: Feedback of my Phd work in Cloudstack Project
Date Thu, 10 Dec 2015 09:11:12 GMT
Before giving feedback I have some questions

1) What do you mean by "correctly predict 60% commits”?
2) What are the feature measures you are giving as input here to system (prediction model)?
3) What kind of output you are expecting?

Web page link you have provided is not working.

> On 10-Dec-2015, at 5:01 AM, Igor Wiese <> wrote:
> Hi, Cloudstack 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 Cloudstack 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 141 commits in which a pair of files changed
> together and could correctly predict 60% 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 constraint, a social network measure obtained from issue
> comments.
> To illustrate, consider the following example from our analysis. For
> release 4.4, the files "cloud/hypervisor/" and
> "cloud/hypervisor/guru/ " changed together in 3 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 the
> previous release (4.3), we were able to predict all 3 commits in which both
> files changed together in release 4.4, and we only issued 0 false
> positives. For this pair of files, the most important contextual
> information was the number of lines of code added, removed and modified in
> each commit,the number of comments in each issue, and social network
> measures (closeness, density, constraint, hierarchy) 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:
> All the best,
> Igor Wiese
> Phd Candidate

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