A Database for the Analysis of Program Change Patterns

Software repositories contain an enormous amount of information regarding the evolution of any large software system. In our experiments we choose the dataset of the freely available Mozilla CVS repository. We downloaded 9552 program files (C++), extracted the CVS log data, and extracted the Mozilla bugs information from the Bugzilla database. From these sources we extracted the program file change data and used a database for storing the extracted data. We further used this database for the analysis of program file changes in order to find change patterns. We apply an approach on the database that allows us to identify the different types of change transactions like bug fixing, clean, bug introducing and bug fix-introducing transactions. We further use the database to find the program file change distribution. Furthermore we use the probability of bug introducing and bug fix-introducing changes to identify the source file as being risky or not for further changes. Such information is not only useful for developers but also for software managers in order to assign resources, e.g., for testing.