API recognition and linking in stack overflow

Stack Overflow, one of the most popular programming Q&A forum has become a wealth of information on software engineering. This textual content provides rich and up-to-date learning resources for developers to learn the usage of application programming interface (API). However, it can be insuffic...

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Main Author: Foo, Chee Yong
Other Authors: Xing Zhenchang
Format: Final Year Project
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/66634
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-666342023-03-03T20:58:56Z API recognition and linking in stack overflow Foo, Chee Yong Xing Zhenchang School of Computer Engineering Emerging Research Lab DRNTU::Engineering::Computer science and engineering::Information systems Stack Overflow, one of the most popular programming Q&A forum has become a wealth of information on software engineering. This textual content provides rich and up-to-date learning resources for developers to learn the usage of application programming interface (API). However, it can be insufficient on its own and developers often need to do further reading on the API documentation. APIs mentioned in natural language texts are rarely fully qualified, so simple matching of the names may be linked to many potential code elements declared by different classes and/or different libraries. In this report, we propose a filter based method of linking API terms in Stack Overflow posts to API documentation by leveraging the context in which an API is mentioned. We also present the implementation of a web application, called LinkAPI, to automatically incorporate online API documentation links into the API terms in Stack Overflow posts. In an evaluation study with three popular Python libraries, we found that our linking technique can resolve most of the ambiguities. Bachelor of Engineering (Computer Science) 2016-04-19T09:03:30Z 2016-04-19T09:03:30Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/66634 en Nanyang Technological University 66 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Information systems
spellingShingle DRNTU::Engineering::Computer science and engineering::Information systems
Foo, Chee Yong
API recognition and linking in stack overflow
description Stack Overflow, one of the most popular programming Q&A forum has become a wealth of information on software engineering. This textual content provides rich and up-to-date learning resources for developers to learn the usage of application programming interface (API). However, it can be insufficient on its own and developers often need to do further reading on the API documentation. APIs mentioned in natural language texts are rarely fully qualified, so simple matching of the names may be linked to many potential code elements declared by different classes and/or different libraries. In this report, we propose a filter based method of linking API terms in Stack Overflow posts to API documentation by leveraging the context in which an API is mentioned. We also present the implementation of a web application, called LinkAPI, to automatically incorporate online API documentation links into the API terms in Stack Overflow posts. In an evaluation study with three popular Python libraries, we found that our linking technique can resolve most of the ambiguities.
author2 Xing Zhenchang
author_facet Xing Zhenchang
Foo, Chee Yong
format Final Year Project
author Foo, Chee Yong
author_sort Foo, Chee Yong
title API recognition and linking in stack overflow
title_short API recognition and linking in stack overflow
title_full API recognition and linking in stack overflow
title_fullStr API recognition and linking in stack overflow
title_full_unstemmed API recognition and linking in stack overflow
title_sort api recognition and linking in stack overflow
publishDate 2016
url http://hdl.handle.net/10356/66634
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