Evaluation of Type Inference with Textual Cues
Amirreza A. Shirani, A. Pastor Lopez-Monroy, Fabio Gonzalez, Thamar Solorio, Mohammad Amin Alipour. NLSE 2018
Type information plays an important role in the success of information retrieval and recommendation systems in software engineering. Thus, the absence of types in dynamically-typed languages poses a challenge to adapt these systems to support dynamic languages.
In this paper, we explore the viability of type inference using textual cues. That is, we formulate the type inference problem as a classification problem which uses the textual features in the source code to predict the type of variables. In this approach, a classifier learns a model to distinguish between types of variables in a program. The model is subsequently used to (approximately) infer the types of other variables.
We evaluate the feasibility of this approach on four Java projects wherein type information is already available in the source code and can be used to train and test a classifier. Our experiments show this approach can predict the type of new variables with relatively high accuracy (80% F-measure). These results suggest that textual cues can be complementary tools in inferring types for dynamic languages.
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