Despite the conventional wisdom concerning the risks related to the use of source code cloning as a software development strategy, several studies appeared in literature indicated that this is not true. In most cases clones are properly maintained and, when this does not happen, is because cloned code evolves independently. Stemming from previous works, this paper combines clone detection and co-change analysis to investigate how clones are maintained when an evolution activity or a bug fixing impact a source code fragment belonging to a clone class. The two case studies reported confirm that, either for bug fixing or for evolution purposes, most of the cloned code is consistently maintained during the same co-change or during temporally close co-changes

Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.

Deeply understanding the intricacies of software must always come before any considerations for modifying it.

Software repositories, such as CVS and Bugzilla, provide a huge amount of data regarding, respectively, source code and change request history. In this paper we propose a study on how change requests have been assigned to developers involved in an open source project and a method to suggest the set of best candidate developers to resolve a new change request. The method is based on the hypothesis that, given a new change request, developers that have resolved similar change requests in the past are the best candidates to resolve the new one. The suggestion can be useful for project managers in order to choose the best candidate to resolve a particular change request and/or to construct a competence database of developers working on software projects. We use the textual description of change requests stored in software repositories to index developers as documents in an information retrieval system. An Information Retrieval method is then applied to retrieve the candidate developers using the textual description of a new change request as a query.Case and evaluation study of the analysis and the methods introduced in this paper has been conducted on two large open source projects, Mozilla and KDE.