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• W2039536418 abstract "Many software development tasks require developers to quickly learn a subset of an Application Programming Interface (API). API learning resources are crucial for helping developers learn an API, but the knowledge relevant to a particular topic of interest may easily be scattered across different documents, which makes finding the necessary information more challenging. This paper proposes an approach to discovering tutorial sections that explain a given API type. At the core of our approach, we classify fragmented tutorial sections using supervised text classification based on linguistic and structural features. Experiments conducted on five tutorials show that our approach is able to discover sections explaining an API type with precision between 0.69 and 0.87 (depending on the tutorial) when trained and tested on the same tutorial. When trained and tested across tutorials, we obtained a precision between 0.74 and 0.94 and lower recall values." @default.
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• W2039536418 date "2015-05-16" @default.
• W2039536418 modified "2023-09-24" @default.
• W2039536418 title "Discovering information explaining API types using text classification" @default.
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• W2039536418 doi "https://doi.org/10.5555/2818754.2818859" @default.
• W2039536418 hasPublicationYear "2015" @default.
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