Hi, Cassandra 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 Cassandra 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 1197 commits in which a pair of files changed together and could correctly predict 48% 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 - median value of closeness, a social network measure obtained from issue comments. To illustrate, consider the following example from our analysis. For release 1.0, the files "cassandra/tools/NodeCmd.java" and "cassandra/tools/NodeProbe.java" changed together in 16 commits. In another 6 commits, 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 all 13 commits in which both files changed together in release 1.0, and we only issued 2 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 words used to describe and discuss the issues and the number of comments in 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/66-cassandra All the best, Igor Wiese Phd Candidate
