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Title:
Detecting API documentation errors
Author: Zhong, Hao (1) ; Su, Zhendong (2)
Keyword: Documentation ; Experimentation ; Reliability ; API documentation error ; Outdated documentation
Source: ACM SIGPLAN Notices
Issued Date: 2013
Volume: 48, Issue:10, Pages:803-815
Indexed Type: SCI ; EI
Department: (1) Institute of Software, Chinese Academy of Sciences, China; (2) University of California, Davis, United States
Abstract: When programmers encounter an unfamiliar API library, they often need to refer to its documentations, tutorials, or discussions on development forums to learn its proper usage. These API documents contain valuable information, but may also mislead programmers as they may contain errors (e.g., broken code names and obsolete code samples). Although most API documents are actively maintained and updated, studies show that many new and latent errors do exist. It is tedious and error-prone to find such errors manually as API documents can be enormous with thousands of pages. Existing tools are ineffective in locating documentation errors because traditional natural language (NL) tools do not understand code names and code samples, and traditional code analysis tools do not understand NL sentences. In this paper, we propose the first approach, DocRef, specifically designed and developed to detect API documentation errors. We formulate a class of inconsistencies to indicate potential documentation errors, and combine NL and code analysis techniques to detect and report such inconsistencies. We have implemented DocRef and evaluated its effectiveness on the latest documentations of five widely-used API libraries. DocRef has detected more than 1,000 new documentation errors, which we have reported to the authors. Many of the errors have already been confirmed and fixed, after we reported them. Copyright © 2013. Copyright © 2013 ACM.
English Abstract: When programmers encounter an unfamiliar API library, they often need to refer to its documentations, tutorials, or discussions on development forums to learn its proper usage. These API documents contain valuable information, but may also mislead programmers as they may contain errors (e.g., broken code names and obsolete code samples). Although most API documents are actively maintained and updated, studies show that many new and latent errors do exist. It is tedious and error-prone to find such errors manually as API documents can be enormous with thousands of pages. Existing tools are ineffective in locating documentation errors because traditional natural language (NL) tools do not understand code names and code samples, and traditional code analysis tools do not understand NL sentences. In this paper, we propose the first approach, DocRef, specifically designed and developed to detect API documentation errors. We formulate a class of inconsistencies to indicate potential documentation errors, and combine NL and code analysis techniques to detect and report such inconsistencies. We have implemented DocRef and evaluated its effectiveness on the latest documentations of five widely-used API libraries. DocRef has detected more than 1,000 new documentation errors, which we have reported to the authors. Many of the errors have already been confirmed and fixed, after we reported them. Copyright © 2013. Copyright © 2013 ACM.
Language: 英语
WOS ID: WOS:000327697300045
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16909
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Zhong, Hao ,Su, Zhendong . Detecting API documentation errors[J]. ACM SIGPLAN Notices,2013-01-01,48(10):803-815.
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