Semi-automatically extracting FAQs to improve accessibility of software development knowledge

Henß, Stefan; Monperrus, Martin; Mezini, Mira

doi:10.1109/icse.2012.6227139

Cited by 26 publications

(35 citation statements)

References 23 publications

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“…More closely related to our goal of bridging the gap between documentation writers and users is the work by Henß et al [21]. They presented an approach for automatically extracting FAQs from software mailing lists and forums through a combination of text mining and NLP.…”

Section: Related Workmentioning

confidence: 99%

Extracting Development Tasks to Navigate Software Documentation

Robillard

Dagenais²

2015

IIEEE Trans. Software Eng.

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Abstract-Knowledge management plays a central role in many software development organizations. While much of the important technical knowledge can be captured in documentation, there often exists a gap between the information needs of software developers and the documentation structure. To help developers navigate documentation, we developed a technique for automatically extracting tasks from software documentation by conceptualizing tasks as specific programming actions that have been described in the documentation. More than 70% of the tasks we extracted from the documentation of two projects were judged meaningful by at least one of two developers. We present TaskNavigator, a user interface for search queries that suggests tasks extracted with our technique in an auto-complete list along with concepts, code elements, and section headers. We conducted a field study in which six professional developers used TaskNavigator for two weeks as part of their ongoing work. We found search results identified through extracted tasks to be more helpful to developers than those found through concepts, code elements, and section headers. The results indicate that task descriptions can be effectively extracted from software documentation, and that they help bridge the gap between documentation structure and the information needs of software developers.

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Section: Related Workmentioning

confidence: 99%

Extracting Development Tasks to Navigate Software Documentation

Robillard

Dagenais²

2015

IIEEE Trans. Software Eng.

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“…Due to the lack of sufficient examples and explanations in API documentation, when that documentation exists, a new research field has emerged, in which methods have been proposed to automatically (re)document APIs [5,8,[44][45][46][47][48][49][50][51]. We refer to documenting APIs as the process of creating documentation for an API, and redocumenting APIs as the process of creating improved documentation based on that which already exists.…”

Section: Related Work (Re)documenting Apismentioning

confidence: 99%

Redocumenting APIs with crowd knowledge: a coverage analysis based on question types

et al. 2016

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Background: Software libraries and frameworks play an important role in software system development. The appropriate usage of their functionalities/components through their APIs, however, is a challenge for developers. Usually, API documentation, when it exists, is insufficient to assist them in their programming tasks. There are few API documentation writers for the many potential readers, resulting in the lack of explanations and examples concerning different scenarios and perspectives. The interaction of developers on the Web, on the other hand, generates content concerning APIs from different perspectives, which can be used to document APIs, also known as crowd documentation. Methods: In this paper, we present a study regarding the knowledge generated by the crowd on the Stack Overflow question-and-answer website. Our main goal is to understand how the crowd can contribute for API documentation on two programming tasks: how to implement a scenario using an API (how-to-do-it), and how to fix domain-independent bugs in an existing code where there was a misunderstanding regarding the usage of an API (debug-corrective). We classified questions available on Stack Overflow by the main concerns of askers, and we used those classified as how-to-do-it and debug-corrective to analyze the coverage of API elements on the discussions related to such questions. Our cases included the well-known and popular Swing and Android APIs. Results: Our main findings showed that the crowd provides more content for debug-corrective tasks than for how-to-do-it tasks, regardless of the API. Android API elements are more discussed by the crowd compared to Swing. Moreover, we observed that some API elements are frequently mentioned together in discussions, and that there is a strong association between API coverage on Stack Overflow and its usage in real software systems. Conclusions: Crowd documentation may not be a complete substitute for official documentation because of its partial coverage, especially for how-to-do-it tasks. However, it can still significantly enhance the existent documentation, especially for the most commonly used API elements, providing code samples and explanations on a large variety of usage nuances. Finally, taking advantage of the high coverage for debug-corrective tasks, a new kind of debugging assistant may be conceived.

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“…Gottipati et al developed a semantic search engine to find relevant posts in software forums [36] . Henβ et al extracted frequently asked questions from mailing lists and internet forums [37] .…”

Section: Studies On Software Information Sitesmentioning

confidence: 99%

TagCombine: Recommending Tags to Contents in Software Information Sites

Wang

Xia

2015

J. Comput. Sci. Technol.

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Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three different components: 1) multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2) similarity-based ranking component which recommends tags from similar objects; 3) tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Freecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recall@10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.

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Semi-automatically extracting FAQs to improve accessibility of software development knowledge

Cited by 26 publications

References 23 publications

Extracting Development Tasks to Navigate Software Documentation

Extracting Development Tasks to Navigate Software Documentation

Redocumenting APIs with crowd knowledge: a coverage analysis based on question types

TagCombine: Recommending Tags to Contents in Software Information Sites

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