An analysis of SNA metrics on the Java Qualitas Corpus

We computed the software graphs of 96 systems of the Java Qualitas Corpus, parsing the source code and identifying the dependencies among classes. We analyzed 12 software metrics on these 96 graphs, nine borrowed from Social Network Analysis (SNA), and three more traditional software metrics, such as Loc, Fan-in and Fan-out. We analyzed their correlations at system level, and studied the correlation statistics at data-set level. Our results show that these correlations are independent from the specific software system and are general properties of Java software systems. We show how the metrics can be partitioned in groups for almost the whole Java Qualitas Corpus, and that such grouping can provide insights on the topology of software networks. For two systems, Eclipse and Netbeans, we computed also the number of bugs, identifying the bugs affecting each class, and finding that some SNA metrics are highly correlated with bugs, while others are strongly anticorrelated. This suggests that practitioners and software engineers might take advantage of such metrics to keep control of software quality.