Abstract: Aiming at the problem that a large number of software packages had no tags or imperfect tags on node package manager (NPM) platform, we proposed a deep learning method based on the seq2seq model to recommend tags for software packages. Firstly, we used ECMAScript tools to analyze the source code of software package, constructed function call graphs of the package, and traversed the function call graph, so as to convert the software package into a set of function call sequences with package semantic information. Secondly, we trained the seq2seq model and applied the trained model to tag recommendation of software package. The trained model could map the function call sequence of package to a group of predicted tag sequence, so as to complete the tag recommendation of software packge. The experimental results show that the method can recommend a reasonable set of tags for the software package, and the accuracy is 82.6%.

Key words: tag recommendation, deep learning, program analysis, attention model