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Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs

Elizabeth Dinella
Ziyang Li
Mayur Naik
Le Song
Ke Wang
International Conference on Learning Representations (2020)


We present a learning-based approach to detect and fix a broad range of bugs in Javascript programs. We frame the problem in terms of learning a sequence of graph transformations: given a buggy program modeled by a graph structure, our model makes a sequence of predictions including the position of bug nodes and corresponding graph edits to produce a fix. Unlike previous works built upon deep neural networks, our approach targets bugs that are more diverse and complex in nature (i.e. bugs that require adding or deleting statements to fix). We have realized our approach in a tool called HOPPITY. By training on 290,715 Javascript code change commits on Github, HOPPITY correctly detects and fixes bugs in 9,490 out of 36,361 programs in an end-to-end fashion. Given the bug location and type of the fix, HOPPITY also outperforms the baseline approach by a wide margin.