M3V: Multi-modal Multi-view Context Embedding for Repair Operator Prediction

Abstract

We address the problem of finding context embeddings for faulty locations to allow a learning-based APR tool to learn and predict the repair operators used at the faulty locations. We introduce M3V, a new multi-modal multi-view context embedding approach, which represents the context of a faulty location in two modalities: (1) texts that capture its signature in a natural language using the tree-LSTM model, and (2) graphs that capture its structure with two views, data and control dependences, using the GNN model. We then fuse these two modalities to learn a probabilistic classifier from correct code that, once given a faulty location, will produce a probabilistic distribution over a set of repair operators. We have evaluated M3V against the state-of-the-art context embedding approaches in repairing two common types of bugs in Java, null pointer exceptions (NPE) and index out of bounds (OOB). Trained and tested with 75673 code samples from 20 real-world projects, a learning-based APR tool can predict repair operators more effectively with our context embeddings in repairing NPE bugs, by achieving higher accuracies (11% – 41%) and higher F1 scores (16% – 143%). For OOB bugs, these improvements are 9% – 30% and 15% – 79%, respectively.