论文标题
基于持续学习的软件修复机器人
A Software-Repair Robot based on Continual Learning
论文作者
论文摘要
软件错误很常见，纠正它们是软件开发和维护过程中成本的很大一部分。这需要自动技术来处理它们。实现这一目标的一个有希望的方向是从历史错误修复示例中获得维修知识。从软件开发历史记录中检索洞察力尤其有吸引力，这对于通过连续集成（CI）生成的机器学习范式的不断进步和飙升的“大”错误修复数据。在本文中，我们提出了R-HERO，这是一种新型的软件维修机器人，该机器人使用持续学习来从源代码更改的连续流中获取错误修复策略，该策略是为单个开发平台Github/Travis CI实现的。我们描述了R-Hero，这是我们学习如何基于持续培训来修复错误的新型系统，我们发现了最初的成功以及对社区的新研究挑战。
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.
