| Summary |
User queries on Stack Overflow commonly suffer from either inadequate length or inadequate clarity with regards to the languages and/or tools they are meant for. Although the site makes use of a tagging system for classifying questions, tags are used minimally (if at all). To investigate the impact of tags in the quality of results returned by the queries, in this research we propose a new query expansion solution. Our technique assigns tags to queries based on how well they match the queries' topics. We evaluated our technique on eight sets of queries categorized by overall length and programming language. We examined the retrieval results by adding varying numbers of tags to the queries, and monitored the recall and precision rates. Our results indicate that queries yield considerably higher recall and precision rates with extra tags than without. We further conclude that tags are a particularly effective means of enhancement when the original queries do not already return sufficient yields to begin with. |
| General note | Presented to the faculty of the Department of Computer Science |
| General note | Advisor: Mark Hills |
| General note | Title from PDF t.p. (viewed September 27, 2023). |
| Dissertation note | M.S. East Carolina University 2022 |
| Bibliography note | Includes bibliographical references. |
| Technical details | System requirements: Adobe Reader. |
| Technical details | Mode of access: World Wide Web. |
| Genre/form | Academic theses. |
| Genre/form | Academic theses. |
