Design patterns are solutions to recurring design problems, conceived to increase benefits in terms of reuse, code quality and, above all, maintainability and resilence to changes. This paper presents results from an empirical study aimed at understanding the evolution of design patterns in three open source systems, namely JHotDraw, ArgoUML, and Eclipse-JDT. Specifically, the study analyzes how frequently patterns are modified, to what changes they undergo and what classes co-change with the patterns. Results show how patterns more suited to support the application purpose tend to change more frequently, and that different kind of changes have a different impact on co-changed classes and a different capability of making the system resilent to changes.

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A version control system, such as CVS/SVN, can provide the history of software changes performed during the evolution of a software project. Among all the changes performed there are some which cause the introduction of bugs, often resolved later with other changes.In this paper we use a technique to identify bug-introducing changes to train a model that can be used to predict if a new change may introduces or not a bug. We represent software changes as elements of a n-dimensional vector space of terms coordinates extracted from source code snapshots.The evaluation of various learning algorithms on a set of open source projects looks very promising, in particular for KNN (K-Nearest Neighbor algorithm) where a significant tradeoff between precision and recall has been obtained.

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