<p>Software metrics are a useful tool for assessing software quality and for making predictions. But currently the interpretation of the measured values is based on personal experience and gut feeling. Not much information is available about thresholds and possible ranges of the metric values. In order to be able to define thresholds on which general recommendations could be based, quantitative data has to be obtain for allowing statistical evaluations and further investigations. So far the collection of test projects requires significant manual interaction for downloading and describing metadata.</p><p>This thesis describes a process for automatically obtaining, storing and maintaining a large number of open software projects from SourceForge.NET [1]. The projects are stored in a local folder structure; the meta-data is stored in a local database. The process is automated as far as possible, repeatable, transparent, extendible and flexible.</p>

Software metrics are a useful tool for assessing software quality and for making predictions. But currently the interpretation of the measured values is based on personal experience and gut feeling. Not much information is available about thresholds and possible ranges of the metric values. In order to be able to define thresholds on which general recommendations could be based, quantitative data has to be obtain for allowing statistical evaluations and further investigations. So far the collection of test projects requires significant manual interaction for downloading and describing metadata. This thesis describes a process for automatically obtaining, storing and maintaining a large number of open software projects from SourceForge.NET [1]. The projects are stored in a local folder structure; the meta-data is stored in a local database. The process is automated as far as possible, repeatable, transparent, extendible and flexible.