Stack Overflow is the most popular Q&A website among software developers. As a platform for knowledge sharing and acquisition, the questions posted in Stack Over- flow usually contain a code snippet. Stack Overflow relies on users to properly tag the programming language of a question and assumes that the programming language of the snippets inside a question is the same as the tag of the question itself. In this the- sis, a classifier is proposed to predict the programming language of questions posted in Stack Overflow using Natural Language Processing (NLP) and Machine Learning (ML). The classifier achieves an accuracy of 91.1% in predicting the 24 most popular programming languages by combining features from the title, body and code snippets of the question. We also propose a classifier that only uses the title and body of the question and has an accuracy of 81.1%. Finally, we propose a classifier of code snip- pets only that achieves an accuracy of 77.7%.Thus, deploying ML techniques on the combination of text and code snippets of a question provides the best performance. These results demonstrate that it is possible to identify the programming language of a snippet of only a few lines of source code. We visualize the feature space of two programming languages Java and SQL in order to identify some properties of the information inside the questions corresponding to these languages. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/10054 |
| Date | 11 September 2018 |
| Creators | Alrashedy, Kamel |
| Contributors | Srinivasan, Venkatesh, Gulliver, T. Aaron |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
Page generated in 0.0021 seconds