A System-Wide Debugging Assistant Powered by Natural Language Processing

Pradeep Dogga, Karthik Narasimhan, Anirudh Sivaraman, Ravi Netravali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationSoCC 2019 - Proceedings of the ACM Symposium on Cloud Computing
PublisherAssociation for Computing Machinery
Pages171-177
Number of pages7
ISBN (Electronic)9781450369732
DOIs
StatePublished - Nov 20 2019
Event10th ACM Symposium on Cloud Computing, SoCC 2019 - Santa Cruz, United States
Duration: Nov 20 2019Nov 23 2019

Publication series

NameSoCC 2019 - Proceedings of the ACM Symposium on Cloud Computing

Conference

Conference10th ACM Symposium on Cloud Computing, SoCC 2019
CountryUnited States
CitySanta Cruz
Period11/20/1911/23/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics

Keywords

  • natural language processing
  • systems debugging

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