A System-Wide Debugging Assistant Powered by Natural Language Processing

Pradeep Dogga, Karthik Narasimhan, Anirudh Sivaraman, R. Netravali
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引用次数: 8

Abstract

Despite advances in debugging tools, systems debugging today remains largely manual. A developer typically follows an iterative and time-consuming process to move from a reported bug to a bug fix. This is because developers are still responsible for making sense of system-wide semantics, bridging together outputs and features from existing debugging tools, and extracting information from many diverse data sources (e.g., bug reports, source code, comments, documentation, and execution traces). We believe that the latest statistical natural language processing (NLP) techniques can help automatically analyze these data sources and significantly improve the systems debugging experience. We present early results to highlight the promise of NLP-powered debugging, and discuss systems and learning challenges that must be overcome to realize this vision.
一个由自然语言处理驱动的系统范围调试助手
尽管调试工具有了进步,但今天的系统调试仍然主要是手工的。开发人员通常遵循一个迭代和耗时的过程,从报告的错误转移到错误修复。这是因为开发人员仍然负责理解系统范围的语义,将现有调试工具的输出和特性连接在一起,并从许多不同的数据源(例如,bug报告、源代码、注释、文档和执行跟踪)中提取信息。我们相信最新的统计自然语言处理(NLP)技术可以帮助自动分析这些数据源,并显着改善系统调试体验。我们展示了早期的结果,以突出nlp驱动调试的前景,并讨论了实现这一愿景必须克服的系统和学习挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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