NL2Type: Inferring JavaScript Function Types from Natural Language Information
Rabee Sohail Malik, Jibesh Patra, Michael Pradel. ICSE 2019
JavaScript is dynamically typed and hence lacks thetype safety of statically typed languages, leading to suboptimal IDE support, difficult to understand APIs, and unexpected run-time behavior. Several gradual type systems have been proposed, e.g., Flow and TypeScript, but they rely on developers to annotatecode with types. This paper presents NL2Type, a learning-based approach for predicting likely type signatures of JavaScript functions. The key idea is to exploit natural language information in source code, such as comments, function names, and parameternames, a rich source of knowledge that is typically ignored by type inference algorithms. We formulate the problem of predicting types as a classification problem and train a recurrent, LSTM-based neural model that, after learning from an annotatedcode base, predicts function types for unannotated code. We evaluate the approach with a corpus of 162,673 JavaScript files from real-world projects. NL2Type predicts types with aprecision of 84.1% and a recall of 78.9% when considering only the top-most suggestion, and with a precision of 95.5% and arecall of 89.6% when considering the top-5 suggestions. The approach outperforms both JSNice, a state-of-the-art approach that analyzes implementations of functions instead of natural language information, and DeepTyper, a recent type prediction approach that is also based on deep learning. Beyond predicting types, NL2Type serves as a consistency checker for existing type annotations. We show that it discovers 39 inconsistencies that deserve developer attention (from a manual analysis of 50 warnings), most of which are due to incorrect type annotations.
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