Promotores: J. Kok, M. Chaudron (Chalmers University)
|Links||Thesis in Leiden Repository|
In this thesis, we address two problems in Software Engineering. The first problem is how to assess the severity of software defects? The second problem we address is that of studying software designs.
The severity of software defects is typically assigned by developers based on their experience. Automated support for assessing the severity of software defects helps human developers to perform this task more efficiently and more accurately. We present a new approach (MAPDESO) for assessing the severity of software defects based on IEEE Standard Classification for Software Anomalies. The novelty of the approach lies in its use of uses ontologies and ontology-based reasoning which links defects to system level quality properties. For validation, the automated MAPDESO prediction method performs well compared to the project classification defects report of the company.
One of the main reasons that makes studying of software designs challenging is the lack of their availability. We decided to collect software designs represented by UML models stored in image formats and use image processing techniques to convert them back to real models. In this thesis we present the 'UML Repository' which contains UML class diagrams (in image and XMI format) and design metrics for these class diagrams. We conducted a series of empirical studies using the UML Repository. These empirical studies are a drop in the ocean empirical studies that can be conducted using the repository. Yet these studies show the versatility of useful studies that can be based on this novel repository of UML designs.