LSTM-based argument recommendation for non-API methods

Abstract

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 ten-fold 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%.