Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open sour...Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open source communi- ties. However, finding the desired software through tags in these communities such as Freecode and ohloh is still chal- lenging because of tag insufficiency. In this paper, we propose TRG （tag recommendation based on semantic graph）, a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effec- tive algorithm to recommend tags for software. With com- prehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.展开更多
文摘Nowadays open source software becomes highly popular and is of great importance for most software engi- neering activities. To facilitate software organization and re- trieval, tagging is extensively used in open source communi- ties. However, finding the desired software through tags in these communities such as Freecode and ohloh is still chal- lenging because of tag insufficiency. In this paper, we propose TRG （tag recommendation based on semantic graph）, a novel approach to discovering and enriching tags of open source software. Firstly, we propose a semantic graph to model the semantic correlations between tags and the words in software descriptions. Then based on the graph, we design an effec- tive algorithm to recommend tags for software. With com- prehensive experiments on large-scale open source software datasets by comparing with several typical related works, we demonstrate the effectiveness and efficiency of our method in recommending proper tags.
