BugDoc: Algorithms to Debug Computational Processes

Raoni Lourenço, Juliana Freire, Dennis Shasha

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

Abstract

Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our experimental data and processing software is available for use, reproducibility, and enhancement.

Original languageEnglish (US)
Title of host publicationSIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages463-478
Number of pages16
ISBN (Electronic)9781450367356
DOIs
StatePublished - Jun 14 2020
Event2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

Conference2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
CountryUnited States
CityPortland
Period6/14/206/19/20

Keywords

  • provenance
  • workflow debugging

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Lourenço, R., Freire, J., & Shasha, D. (2020). BugDoc: Algorithms to Debug Computational Processes. In SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (pp. 463-478). (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/3318464.3389763