Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/70276
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dc.contributor.authorTan, Ri Sheng
dc.date.accessioned2017-04-18T07:28:38Z
dc.date.available2017-04-18T07:28:38Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/70276
dc.description.abstractIn the field of software engineering, people often visit forums to exchange information, to seek or give advice. The provision of such rich and meaningful content allows software developers to understand the use of Application Programming Interface (API) but many of these developers may find the content insufficient and require additional explanation from the API documentation. Fortunately, for some of the APIs mentioned in forum posts, it is manually linked by forum users, readers can click on the links to visit its official API documentations for explanation and save themselves from performing a manual search on search engines for the API documentations. However, for those APIs that are mentioned but are not manually linked by forum users, readers will need to perform a manual search on search engines for the API documentation, wasting time and resources which can be avoided by automatically linking the sentence that contains an API mention to its API documentation. The prerequisite to linking a sentence that contains an API mention to its official API documentation is the extraction of the fully qualified API name of the API mentioned, as establishing such link is only possible with the fully qualified API name known. In this report, we considered two possible API extraction methods – IF-THEN rules and Naïve Bayes – to perform extraction of a fully qualified API name from a sentence. After weighing the pros and cons, we designed and developed a Naïve Bayes API extraction method. The Naïve Bayes API extraction method was evaluated against two baseline methods based on the fully qualified API names collected from four popular Python libraries, of which Naïve Bayes API extraction method outperformed the two baseline methods.en_US
dc.format.extent62 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Information systemsen_US
dc.titleAuto-documentation for stack overflowen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Shang-Weien_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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