Software bugs in a production environment have an undesirable impact on quality of service, unplanned system downtime, and disruption in good customer experience, resulting in loss of revenue and reputation. Existing approaches to automated software bug repair focuses on known bug templates detected using static code analysis tools and test suites, and in automatic generation of patch code for these bugs. We describe the typical bug fixing process employed in the Linux kernel, and motivate the need for a new automated tool flow to fix bugs. We present an initial design of such an automated tool that uses Recurrent Neural Network (RNN) based Natural Language Processing to generate patch recommendations from user generated bug reports. At the 50th percentile of the test bugs, the correct patch occurs within the top 11.5 patch recommendations output by the model. Further, we present a Linux kernel developer's assessment of the quality of patches recommended for new unresolved kernel bugs.

中文翻译：

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基于循环神经网络的 Linux 内核错误补丁推荐器

生产环境中的软件错误会对服务质量、计划外的系统停机和良好的客户体验产生不良影响，从而导致收入和声誉的损失。现有的自动化软件错误修复方法侧重于使用静态代码分析工具和测试套件检测到的已知错误模板，以及为这些错误自动生成补丁代码。我们描述了 Linux 内核中使用的典型错误修复过程，并激发了对新的自动化工具流来修复错误的需求。我们提出了这种自动化工具的初步设计，该工具使用基于循环神经网络 (RNN) 的自然语言处理从用户生成的错误报告中生成补丁建议。在测试错误的第 50 个百分位数处，正确的补丁出现在前 11 个中。模型输出的 5 个补丁建议。此外，我们还介绍了 Linux 内核开发人员对为新的未解决内核错误推荐的补丁质量的评估。