Abstract

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 that use deep neural networks, our approach targets bugs that are more complex and semantic in nature (i.e. bugs that require adding or deleting statements to fix). We have realized our approach in a tool called H OPPITY. By training on 290,715 Javascript code change commits on Github, H OPPITY 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, H OPPITY also outperforms the baseline approach by a wide margin.