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Automatic code completion is one of the most useful features provided by advanced IDEs.Argument recommendation,as a special kind of code completion,is widely used as well.While existing approaches focus on argument recommendation for popular APIs,a large number of non-API invocations are requesting for accurate argument recommendation as well.To this end,we propose an LSTM-based approach to recommending non-API arguments instantly when method calls are typed in.With data collected from a large corpus of open-source applications,we train an LSTM neural network to recommend actual arguments based on identifiers of the invoked method,the corresponding formal parameter,and a list of syntactically correct candidate arguments.To feed these identifiers into the LSTM neural network,we convert them into fixed-length vectors by Paragraph Vector,an unsupervised neural network based learning algorithm.With the resulting LSTM neural network trained on sample applications,for a given call site we can predict which of the candidate arguments is more likely to be the correct one.We evaluate the proposed approach with tenfold validation on 85 open-source C applications.Results suggest that the proposed approach outperforms the state-of-the-art approaches in recommending non-API arguments.It improves the precision significantly from 71.46％ to 83.37％.