Recommending reference API documentation

Reference documentation is an important source of information on API usage. However, information useful to programmers can be buried in irrelevant text, or attached to a non-intuitive API element, making it difficult to discover. We propose to detect and recommend fragments of API documentation pote...

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Vydáno v:Empirical software engineering : an international journal Ročník 20; číslo 6; s. 1558 - 1586
Hlavní autoři: Robillard, Martin P., Chhetri, Yam B.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.12.2015
Springer Nature B.V
Témata:
Application programming interface
Compilers
Computer Science
Documentation
Fragments
Interpreters
Programming Languages
Recommender systems
Software Engineering/Programming and Operating Systems
Recommendation systems
Natural language processing
API documentation
Application programming interfaces
Text classification
ISSN:1382-3256, 1573-7616
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Popis
Shrnutí:Reference documentation is an important source of information on API usage. However, information useful to programmers can be buried in irrelevant text, or attached to a non-intuitive API element, making it difficult to discover. We propose to detect and recommend fragments of API documentation potentially important to a programmer who has already decided to use a certain API element. We categorize text fragments in API documentation based on whether they contain information that is indispensable , valuable , or neither. From the fragments that contain knowledge worthy of recommendation, we extract word patterns, and use these patterns to automatically find new fragments that contain similar knowledge in unseen documentation. We implemented our technique in a tool, Krec, that supports both information filtering and discovery. In an evaluation study with randomly-sampled method definitions from ten open source systems, we found that with a training set derived from about 1000 documentation units, we could issue recommendations with 90 % precision and 69 % recall. In a study involving ten independent assessors, indispensable knowledge items recommended for API types were judged useful 57 % of the time and potentially useful an additional 30 % of the time.
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ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-014-9323-y