The term "software entropy" refers to the tendency for software, over time, to become difficult and costly to maintain. A software system that undergoes continuous change, such as having new functionality added to its original design, will eventually become more complex and can become disorganized as it grows, losing its original design structure. A recent study show that software degradation may be measured using the WMC expressed in terms of Shannon entropy. In this paper we extended the empirical analyses also to RFC and CBO since these CK metrics have been shown to be correlated with fault-proneness of OO classes. We analyzed various releases of the publicly available Eclipse and Netbeans software systems, calculating the entropy of some CK metrics for every release analyzed. The validity is shown through a direct measure of software quality such as the number of detected defects. Our results display a very good correlation between the entropy of CBO and RFC and the number of bugs for Eclipse and Netbeans. Complexity and quality metrics are in general computed on every system module while the entropy is just a scalar number that characterizes a whole system, this result suggests that the entropy of some CK metrics could be considered as a global quality metric for large software systems. Our results need, however, to be confirmed for other large software systems.

The distribution of bugs in software systems has been shown to satisfy the Pareto principle, and typically shows a power-law tail when analyzed as a rank-frequency plot. In a recent paper, Zhang showed that the Weibull cumulative distribution is a very good fit for the Alberg diagram of bugs built with experimental data. In this paper, we further discuss the subject from a statistical perspective, using as case studies five versions of Eclipse, to show how log-normal, Double-Pareto, and Yule-Simon distributions may fit the bug distribution at least as well as the Weibull distribution. In particular, we show how some of these alternative distributions provide both a superior fit to empirical data and a theoretical motivation to be used for modeling the bug generation process. While our results have been obtained on Eclipse, we believe that these models, in particular the Yule-Simon one, can generalize to other software systems.

We analyze the process of software development for large, object oriented, open source software systems. Such systems may be described as complex networks when suitable variables are properly identified. In particular we model the system growth through a Yule process which enable us to fit data extracted from freely available repositories. We consider quantities related to relevant properties of the software system itself, like methods names, instance variable names, number of subclasses of each class. As opposite to a plain analysis of a final product, we perform a dynamic analysis of the product evolution, since we look at how system properties change during the development along different releases. The final goal is to detect statistical features that may be related to software dependability.