Software team members, as they communicate and coordinate their work with others throughout the life-cycle of their projects, generate different kinds of textual artifacts. Despite the variety of works in the area of mining software artifacts, relatively little research has focused on communication artifacts. Software communication artifacts, in addition to source code artifacts, contain useful semantic information that is not fully explored by existing approaches.
This thesis, presents the development of a text analysis method and tool to extract and represent useful pieces of information from a wide range of textual data sources associated with software projects. Our text analysis system integrates Natural Language Processing techniques and statistical text analysis methods, with software domain knowledge. The extracted information is represented as RDF-style triples which constitute interesting relations between developers and software products. We applied the developed system to analyze five different textual information, i.e., source code commits, bug reports, email messages, chat logs, and wiki pages. In the evaluation of our system, we found its precision to be 82%, its recall 58%, and its F-measure 68%.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1776 |
| Date | 06 1900 |
| Creators | Hasan, Maryam |
| Contributors | Stroulia, Eleni (Computing Science), Barbosa, Denilson (Computing Science), Wong, Ken (Computing Science), Reformat, Marek (Electrical and Computer Engineering) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 1316990 bytes, application/pdf |
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