Object-oriented software metrics: a practical guide
Object-oriented software metrics: a practical guide
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Understanding the shape of Java software
Proceedings of the 21st annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications
Power-Laws in a Large Object-Oriented Software System
IEEE Transactions on Software Engineering
Predicting defects using network analysis on dependency graphs
Proceedings of the 30th international conference on Software engineering
ACM Transactions on Software Engineering and Methodology (TOSEM)
On the Distribution of Software Faults
IEEE Transactions on Software Engineering
An Empirical Study of Unused Design Decisions in Open Source Java Software
APSEC '08 Proceedings of the 2008 15th Asia-Pacific Software Engineering Conference
Validation of network measures as indicators of defective modules in software systems
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Assessing traditional and new metrics for object-oriented systems
Proceedings of the 2010 ICSE Workshop on Emerging Trends in Software Metrics
An empirical study of social networks metrics in object-oriented software
Advances in Software Engineering - Special issue on new generation of software metrics
| Hi-index | 0.00 |
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.