Andi Wahju Rahardjo Emanuel

Modularity Index Metrics for Java-Based Open Source Software Projects

Open Source Software (OSS) Projects are gaining popularity these days, and they become alternatives in building software system. Despite many failures in these projects, there are some success stories with one of the identified success factors is modularity. This paper presents the first quantitative software metrics to measure modularity level of Java-based OSS Projects called Modularity Index. This software metrics is formulated by analyzing modularity traits such as size, complexity, cohesion, and coupling of 59 Java-based OSS Projects from sourceforge.net using SONAR tool. These OSS Projects are selected since they have been downloaded more than 100K times and believed to have the required modularity trait to be successful. The software metrics related to modularity in class, package and system level of these projects are extracted and analyzed. The similarities found are then analyzed to determine the class quality, package quality, and then combined with system architecture measure to formulate the Modularity Index. The case study of measuring Modularity Index during the evolution of JFreeChart project has shown that this software metrics is able to identify strengths and potential problems of the project.

Statistical analysis of popular open source software projects and their communities

Open Source Software (OSS) becomes one of the mainstream software development methodology competing with commercial and proprietary software development. One of the distinct characteristics of OSS projects is the existence group of contributors who joined the project voluntarily called OSS Communities. In this study, the statistical analysis of 263 popular OSS Projects and their communities is performed. The popularity of the OSS Projects is determined from suggestion from selected websites found from Google search engine. The analysis covers information such as OSS Projects name, description, category, repository, community type, number of contributors, and the start year. There are four important findings of the statistical analysis. First finding is that most of the category of the OSS Projects is computer and networking related. The other findings are that most of the project is in Ad Hoc state and the different trends in the number of contributors in Foundation and Commercial Company of OSS Communities. The last finding is that most of the OSS Projects are using Github, Sourceforge and Ohloh as their source code repositories. These results provide important insights about the structure and activities of OSS Projects and their communities.