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Debugging in the (very) large: ten years of implementation and experience

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Kinshuman Kinshumann,

Galen HuntAuthors Info & Claims

Communications of the ACM, Volume 54, Issue 7

Pages 111 - 116

https://doi.org/10.1145/1965724.1965749

Published: 01 July 2011 Publication History

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Abstract

Windows Error Reporting (WER) is a distributed system that automates the processing of error reports coming from an installed base of a billion machines. WER has collected billions of error reports in 10 years of operation. It collects error data automatically and classifies errors into buckets, which are used to prioritize developer effort and report fixes to users. WER uses a progressive approach to data collection, which minimizes overhead for most reports yet allows developers to collect detailed information when needed. WER takes advantage of its scale to use error statistics as a tool in debugging; this allows developers to isolate bugs that cannot be found at smaller scale. WER has been designed for efficient operation at large scale: one pair of database servers records all the errors that occur on all Windows computers worldwide.

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Medeiros MKulesza UCoelho RBonifacio RTreude CBarbosa ERoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)The Impact Of Bug Localization Based on Crash Report Mining: A Developers' PerspectiveProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639730(13-24)Online publication date: 14-Apr-2024
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Su FKwasnick RHolm JPenner WGartler HBoelter JZhou YArbab BRothberg M(2024)Product Health Insights Using TelemetryIEEE Design & Test10.1109/MDAT.2023.331733741:4(56-64)Online publication date: Aug-2024
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Bheree MAnvik J(2024)Identifying and Detecting Inaccurate Stack Traces in Bug Reports2024 7th International Conference on Software and System Engineering (ICoSSE)10.1109/ICoSSE62619.2024.00010(9-14)Online publication date: 19-Apr-2024
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Index Terms

Debugging in the (very) large: ten years of implementation and experience
1. General and reference
  1. Cross-computing tools and techniques
    1. Reliability
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Extra-functional properties
      1. Software reliability

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Published In

Communications of the ACM Volume 54, Issue 7

July 2011

133 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1965724

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 July 2011

Published in CACM Volume 54, Issue 7

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Cited By

View all

Medeiros MKulesza UCoelho RBonifacio RTreude CBarbosa ERoychoudhury APaiva AAbreu RStorey MAniche MNagappan N(2024)The Impact Of Bug Localization Based on Crash Report Mining: A Developers' PerspectiveProceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice10.1145/3639477.3639730(13-24)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3639477.3639730
Su FKwasnick RHolm JPenner WGartler HBoelter JZhou YArbab BRothberg M(2024)Product Health Insights Using TelemetryIEEE Design & Test10.1109/MDAT.2023.331733741:4(56-64)Online publication date: Aug-2024
https://doi.org/10.1109/MDAT.2023.3317337
Bheree MAnvik J(2024)Identifying and Detecting Inaccurate Stack Traces in Bug Reports2024 7th International Conference on Software and System Engineering (ICoSSE)10.1109/ICoSSE62619.2024.00010(9-14)Online publication date: 19-Apr-2024
https://doi.org/10.1109/ICoSSE62619.2024.00010
Murali VGross LQian RChandra SEldh SFalessi D(2021)Industry-scale IR-based bug localizationProceedings of the 43rd International Conference on Software Engineering: Software Engineering in Practice10.1109/ICSE-SEIP52600.2021.00028(188-197)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1109/ICSE-SEIP52600.2021.00028
Pradel MMurali VQian RMachalica MMeijer EChandra SKhurshid SPăsăreanu C(2020)Scaffle: bug localization on millions of filesProceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3395363.3397356(225-236)Online publication date: 18-Jul-2020
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Medeiros MKulesza UBonifacio RAdachi ECoelho R(2020)Improving Bug Localization by Mining Crash Reports: An Industrial Study2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME46990.2020.00086(766-775)Online publication date: Sep-2020
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Guo ZLi YMa WZhou YLu HChen LXu B(2020)Boosting crash-inducing change localization with rank-performance-based feature subset selectionEmpirical Software Engineering10.1007/s10664-020-09802-125:3(1905-1950)Online publication date: 1-May-2020
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K. NIslam MHamou-Lhadj AHamdaqa MMindel MMüller HOnut V(2016)An effective method for detecting duplicate crash reports using crash traces and hidden Markov modelsProceedings of the 26th Annual International Conference on Computer Science and Software Engineering10.5555/3049877.3049885(75-84)Online publication date: 31-Oct-2016
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