Detecting API documentation errors

ISCAS OpenIR

	Detecting API documentation errors
	Zhong, Hao (1); Su, Zhendong (2)
	2013
发表期刊	ACM SIGPLAN Notices
ISSN	15232867
卷号	48 期号:10 页码:803-815
摘要	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.; 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.
收录类别	SCI ; EI
关键词	Documentation Experimentation Reliability Api Documentation Error Outdated Documentation
部门归属	(1) Institute of Software, Chinese Academy of Sciences, China; (2) University of California, Davis, United States
语种	英语
WOS记录号	WOS:000327697300045
引用统计	被引频次：66[WOS] [WOS记录] [WOS相关记录]
内容类型	期刊论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16909
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Zhong, Hao ,Su, Zhendong . Detecting API documentation errors[J]. ACM SIGPLAN Notices,2013,48(10):803-815.
APA	Zhong, Hao ,&Su, Zhendong .(2013).Detecting API documentation errors.ACM SIGPLAN Notices,48(10),803-815.
MLA	Zhong, Hao ,et al."Detecting API documentation errors".ACM SIGPLAN Notices 48.10(2013):803-815.