Runtime exceptions are inevitable parts of software systems. While developers often write exception handling code to avoid the severe outcomes of these exceptions, such code is most effective if accompanied by accurate runtime exception types. Predicting the runtime exceptions that may occur in a program, however, is difficult as the situations that lead to these exceptions are complex. We propose D-REX (Deep Runtime EXception detector), as an approach for predicting runtime exceptions of Java methods based on the static properties of code. 
The core of D-REX is a machine learning model that leverages the representation learning ability of neural networks to infer a set of signals from code to predict the related runtime exception types. This model, which we call Location Aware Transformer, adapts a state-of-the-art language model, Transformer, to provide accurate predictions for the exception types, as well as interpretable recommendations for the exception prone elements of code. We curate a benchmark dataset of 200,000 Java projects from GitHub to train and evaluate D-REX. Experiments demonstrate that D-REX predicts runtime exception types with 81% of Top 1 accuracy, outperforming multiple non-Transformer baselines by a margin of at least 12%. Furthermore, it can predict the exception prone elements of code with 75% Top 1 precision.

D-REX: Static Detection of Relevant Runtime Exceptions with Location Aware Transformer

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