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.

We present a comprehensive study of an implementation of the Smalltalk object oriented system, one of the first and purest object-oriented programming environment, searching for scaling laws in its properties. We study ten system properties, including the distributions of variable and method names, inheritance hierarchies, class and method sizes, system architecture graph. We systematically found Pareto - or sometimes log-normal - distributions in these properties. This denotes that the programming activity, even when modeled from a statistical perspective, can in no way be simply modeled as a random addition of independent increments with finite variance, but exhibits strong organic dependencies on what has been already developed. We compare our results with similar ones obtained for large Java systems, reported in the literature or computed by ourselves for those properties never studied before, showing that the behavior found is similar in all studied object oriented systems. We show how the Yule process is able to stochastically model the generation of several of the power-laws found, identifying the process parameters and comparing theoretical and empirical tail indexes. Lastly, we discuss how the distributions found are related to existing object-oriented metrics, like Chidamber and Kemerer's, and how they could provide a starting point for measuring the quality of a whole system, versus that of single classes. In fact, the usual evaluation of systems based on mean and standard deviation of metrics can be misleading. It is more interesting to measure differences in the shape and coefficients of the data?s statistical distributions.

We describe the Extreme Programming Ontology (XPO), a formal model specifying the main concepts used in the extreme programming methodology and their properties. XPO's modular structure was developed using the usual normative top down approach to software engineering process modeling. It relies on a set of core components rooted in three main concepts: organisational role, product and phase. Besides being useful for indexing relevant documents and XP artifacts such as user stories and Wiki pages, XPO is aimed at being a sound basis for nonintrusive analysis of agile processes, mining process data about programmers activity and repositories content in order to extract new concepts potentially identifying critical factors in agile software development. Extension to XPO are also discussed, including other agile methodologies and more general software engineering concepts.