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• W2588451970 abstract "Although efforts have been made on discovering and searching API usage patterns, how to categorize and recommend follow-up API usage patterns is still largely unexplored. This paper advances the state-of-the-art by proposing two methods for categorizing and recommending API usage patterns: first, categories of the usage patterns are automatically identified based on a proposed degree centrality-based clustering algorithm, and second, follow-up usage patterns of an adopted pattern are recommended based on a proposed metric of measuring distances between patterns. In the experimental evaluations, the patterns categorization can achieve 85.4% precision rate with 83% recall rate. The patterns recommendation had approximately half a chance of correctly predicting the follow-up patterns that were actually used by the programmers." @default.
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• W2588451970 date "2016-12-01" @default.
• W2588451970 modified "2023-09-24" @default.
• W2588451970 title "Categorizing and Recommending API Usage Patterns Based on Degree Centralities and Pattern Distances" @default.
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• W2588451970 doi "https://doi.org/10.1109/ics.2016.0120" @default.
• W2588451970 hasPublicationYear "2016" @default.
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