Observational Studies of Software Engineering Using Data

OBSERVATIONAL STUDIES OF SOFTWARE ENGINEERING USING DATA FROM SOFTWARE REPOSITORIES Daniel Pierce Delorey Department of Computer Science Master of Science Data for empirical studies of software engineering can be difficult to obtain. Extrapolations from small controlled experiments to large development environments are tenuous and observation tends to change the behavior of the subjects. In this thesis we propose the use of data gathered from software repositories in observational studies of software engineering. We present tools we have developed to extract data from CVS repositories and the SourceForge Research Archive. We use these tools to gather data from 9,999 Open Source projects. By analyzing these data we are able to provide insights into the structure of Open Source projects. For example, we find that the vast majority of the projects studied have never had more than three contributors and that the vast majority of authors studied have never contributed to more than one project. However, there are projects that have had up to 120 contributors in a single year and authors who have contributed to more than 20 projects which raises interesting questions about team dynamics in the Open Source community. We also use these data to empirically test the belief that productivity is constant in terms of lines of code per programmer per year regardless of the programming language used. We find that yearly programmer productivity is not constant across programming languages, but rather that developers using higher level languages tend to write fewer lines of code per year than those using lower level languages.