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

Henß, Stefan and Monperrus, Martin and Mezini, Mira :
Semi-automatically extracting FAQs to improve accessibility of software development knowledge.
In: ICSE '12 . IEEE Press
[Conference or Workshop Item] , (2012)

Abstract

Frequently asked questions (FAQs) are a popular way to document software development knowledge. As creating such documents is expensive, this paper presents an approach for automatically extracting FAQs from sources of software development discussion, such as mailing lists and Internet forums, by combining techniques of text mining and natural language processing. We apply the approach to popular mailing lists and carry out a survey among software developers to show that it is able to extract high-quality FAQs that may be further improved by experts.

Item Type: Conference or Workshop Item
Erschienen: 2012
Creators: Henß, Stefan and Monperrus, Martin and Mezini, Mira
Title: Semi-automatically extracting FAQs to improve accessibility of software development knowledge
Language: German
Abstract:

Frequently asked questions (FAQs) are a popular way to document software development knowledge. As creating such documents is expensive, this paper presents an approach for automatically extracting FAQs from sources of software development discussion, such as mailing lists and Internet forums, by combining techniques of text mining and natural language processing. We apply the approach to popular mailing lists and carry out a survey among software developers to show that it is able to extract high-quality FAQs that may be further improved by experts.

Title of Book: Proceedings of the 34th International Conference on Software Engineering
Series Name: ICSE '12
Publisher: IEEE Press
Uncontrolled Keywords: text mining, natural language processing, NLP, software developement management, knowledge management
Divisions: Profile Areas
Profile Areas > Cybersecurity (CYSEC)
Event Location: Zurich, Switzerland
Date Deposited: 28 Aug 2017 13:34
Identification Number: TUD-CS-2012-0381
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