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Recent advances in neural modeling for bug detection have been verypromising. More specifically, using snippets of code to create continuousvectors or \textit{embeddings} has been shown to be very good at method nameprediction and claimed to be efficient at other tasks, such as bug detection.However, to this end, the method has not been empirically tested for thelatter. In this work, we use the Code2Vec model of Alon et al. to evaluate itfor detecting off-by-one errors in Java source code. We define bug detection asa binary classification problem and train our model on a large Java file corpuscontaining likely correct code. In order to properly classify incorrect code,the model needs to be trained on false examples as well. To achieve this, wecreate likely incorrect code by making simple mutations to the original corpus.Our quantitative and qualitative evaluations show that an attention-based modelthat uses a structural representation of code can be indeed successfully usedfor other tasks than method naming.

Using Distributed Representation of Code for Bug Detection