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Mining the Evolution of Software Component Usage


Mileva,  Yana
International Max Planck Research School, MPI for Informatics, Max Planck Society;


Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Mileva, Y. (2012). Mining the Evolution of Software Component Usage. PhD Thesis, Universität des Saarlandes, Saarbrücken.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0027-9F5C-B
The topic of this thesis is the analysis of the evolution of software components. In order to track the evolution of software components, one needs to collect the evolution information of each component. This information is stored in the version control system (VCS) of the project�the repository of the history of events happening throughout the project�s lifetime. By using software archive mining techniques one can extract and leverage this information. The main contribution of this thesis is the introduction of evolution usage trends and evolution change patterns. The raw information about the occurrences of each component is stored in the VCS of the project. By organizing it in evolution trends and patterns, we are able to draw conclusions and issue recommendations concerning each individual component and the project as a whole. Evolution Trends An evolution trend is a way to track the evolution of a software component throughout the span of the project. The trend shows the increases and decreases in the usage of a specific component, which can be indicative of the quality of this component. AKTARI is a tool, presented in this thesis, that is based on such evolution trends and can be used by the software developers to observe and draw conclusions about the behavior of their project. Evolution Patterns An evolution pattern is a pattern of a frequently occurring code change throughout the span of the project. Those frequently occurring changes are project-specific and are explanatory of the way the project evolves. Each such evolution pattern contains in itself the specific way �things are done� in the project and as such can serve for defect detection and defect prevention. The technique of mining evolution patterns is implemented as a basis for the LAMARCK tool, presented in this thesis.