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Software Documentation Issues Unveiled.

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Title: Software Documentation Issues Unveiled.
Authors: Aghajani, Emad1, Nagy, Csaba1, Vega-Márquez, Olga Lucero2, Linares-Vásquez, Mario2, Moreno, Laura3, Bavota, Gabriele1, Lanza, Michele1
Source: ICSE: International Conference on Software Engineering. 5/25/2019, p1199-1210. 12p.
Subjects: Software documentation, Artificial intelligence, Computer science, Software engineering, Computer software development
Abstract: (Good) Software documentation provides developers and users with a description of what a software system does, how it operates, and how it should be used. For example, technical documentation (e.g., an API reference guide) aids developers during evolution/maintenance activities, while a user manual explains how users are to interact with a system. Despite its intrinsic value, the creation and the maintenance of documentation is often neglected, negatively impacting its quality and usefulness, ultimately leading to a generally unfavorable take on documentation. Previous studies investigating documentation issues have been based on surveying developers, which naturally leads to a somewhat biased view of problems affecting documentation. We present a large scale empirical study, where we mined, analyzed, and categorized 878 documentation-related artifacts stemming from four different sources, namely mailing lists, Stack Overflow discussions, issue repositories, and pull requests. The result is a detailed taxonomy of documentation issues from which we infer a series of actionable proposals both for researchers and practitioners. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Software Documentation Issues Unveiled.
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  Data: <searchLink fieldCode="DE" term="%22Software+documentation%22">Software documentation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink>
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  Data: (Good) Software documentation provides developers and users with a description of what a software system does, how it operates, and how it should be used. For example, technical documentation (e.g., an API reference guide) aids developers during evolution/maintenance activities, while a user manual explains how users are to interact with a system. Despite its intrinsic value, the creation and the maintenance of documentation is often neglected, negatively impacting its quality and usefulness, ultimately leading to a generally unfavorable take on documentation. Previous studies investigating documentation issues have been based on surveying developers, which naturally leads to a somewhat biased view of problems affecting documentation. We present a large scale empirical study, where we mined, analyzed, and categorized 878 documentation-related artifacts stemming from four different sources, namely mailing lists, Stack Overflow discussions, issue repositories, and pull requests. The result is a detailed taxonomy of documentation issues from which we infer a series of actionable proposals both for researchers and practitioners. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/ICSE.2019.00122
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        Text: English
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      – SubjectFull: Artificial intelligence
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              Text: 5/25/2019
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