论文标题
linux内核虫的基于神经网络的循环贴剂建议剂
A Recurrent Neural Network Based Patch Recommender for Linux Kernel Bugs
论文作者
论文摘要
生产环境中的软件错误会对服务质量，计划外系统停机时间以及良好客户体验的破坏产生不良影响，从而导致收入和声誉丧失。现有的自动化软件错误维修方法的方法集中在使用静态代码分析工具和测试套件中检测到的已知错误模板，以及这些错误的自动生成补丁代码。我们描述了Linux内核中采用的典型错误修复过程，并激发了需要新的自动化工具流以修复错误的需求。我们提供了这种自动化工具的初始设计，该工具使用基于复发的神经网络（RNN）自然语言处理来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个百分点时，正确的补丁发生在模型输出的前11.5个补丁建议中。此外，我们介绍了Linux内核开发人员对建议新的未解决内核错误建议的补丁质量的评估。
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.
