A Software-Repair Robot based on Continual Learning

TL;DR

This paper introduces R-Hero, a continual learning-based software repair robot that learns bug fixing strategies from ongoing source code changes on GitHub/Travis CI, aiming to automate bug correction.

Contribution

The paper presents R-Hero, a novel system applying continual learning to software repair, leveraging continuous streams of code changes for automatic bug fixing.

Findings

Initial successes in bug fixing using R-Hero

Identification of new research challenges in continual learning for software repair

Demonstration of learning from real-world CI data

Abstract

Software bugs are common and correcting them accounts for a significant part of costs in the software development and maintenance process. This calls for automatic techniques to deal with them. One promising direction towards this goal is gaining repair knowledge from historical bug fixing examples. Retrieving insights from software development history is particularly appealing with the constant progress of machine learning paradigms and skyrocketing `big' bug fixing data generated through Continuous Integration (CI). In this paper, we present R-Hero, a novel software repair bot that applies continual learning to acquire bug fixing strategies from continuous streams of source code changes, implemented for the single development platform Github/Travis CI. We describe R-Hero, our novel system for learning how to fix bugs based on continual training, and we uncover initial successes as well as novel research challenges for the community.