# An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

## TL;DR

This paper investigates the feasibility of using neural machine translation to automatically learn and generate bug-fixing patches from large-scale open-source code repositories, demonstrating promising results in real-world scenarios.

## Contribution

It presents an empirical study that mines extensive bug-fix data, trains a neural translation model, and evaluates its effectiveness in fixing bugs in the wild.

## Key findings

- The model can fix thousands of unique buggy methods.
- Predicts correct patches in 9-50% of cases.
- Generates patches rapidly and emulates AST operations.

## Abstract

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub, in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9-50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.