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Creators/Authors contains: "Dogga, Pradeep"

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  1. ; ; ; ; ; (, USENIX Annual Technical Conference)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proceedings of Machine Learning and Systems 4 (MLSys 2022))
    Accepted Manuscript Full Text Available
  3. ; ; ; (, Proceedings of the ACM Symposium on Cloud Computing)
    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. 
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