research-article

Understanding and detecting real-world performance bugs

Authors:

Joel Scherpelz, and

Shan LuAuthors Info & Claims

ACM SIGPLAN Notices, Volume 47, Issue 6

Pages 77 - 88

https://doi.org/10.1145/2345156.2254075

Published: 11 June 2012 Publication History

Abstract

Developers frequently use inefficient code sequences that could be fixed by simple patches. These inefficient code sequences can cause significant performance degradation and resource waste, referred to as performance bugs. Meager increases in single threaded performance in the multi-core era and increasing emphasis on energy efficiency call for more effort in tackling performance bugs.

This paper conducts a comprehensive study of 110 real-world performance bugs that are randomly sampled from five representative software suites (Apache, Chrome, GCC, Mozilla, and MySQL). The findings of this study provide guidance for future work to avoid, expose, detect, and fix performance bugs.

Guided by our characteristics study, efficiency rules are extracted from 25 patches and are used to detect performance bugs. 332 previously unknown performance problems are found in the latest versions of MySQL, Apache, and Mozilla applications, including 219 performance problems found by applying rules across applications.

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Cited By

Khurram SSabau ALichter HTanachutiwat S(2024)Scenario-based synthetic traffic generation for web applications using workload patternsProceedings of the 2024 6th Asia Pacific Information Technology Conference10.1145/3651623.3651638(22-30)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3651623.3651638
Zhao GGeorgiou SHassan SZou YTruong DCorbin TSpinellis DConstantinou EBacchelli A(2024)Enhancing Performance Bug Prediction Using Performance Code MetricsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644920(50-62)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644920
Khosravi Tabrizi AEzzati-Jivan NTetreault FBalsamo SKnottenbelt WAbad CShang W(2024)An Adaptive Logging System (ALS): Enhancing Software Logging with Reinforcement Learning TechniquesProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645033(37-47)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645033
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Index Terms

Understanding and detecting real-world performance bugs
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance
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 performance

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Information & Contributors

Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 47, Issue 6

PLDI '12

June 2012

534 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2345156

Issue’s Table of Contents

PLDI '12: Proceedings of the 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation
June 2012
572 pages
ISBN:9781450312059
DOI:10.1145/2254064
General Chairs:
Jan Vitek
Purdue University
,
Haibo Lin
Microsoft China
,
Program Chair:
Frank Tip
IBM T.J. Watson Research Center

Copyright © 2012 ACM.

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Association for Computing Machinery

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Publication History

Published: 11 June 2012

Published in SIGPLAN Volume 47, Issue 6

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Cited By

Khurram SSabau ALichter HTanachutiwat S(2024)Scenario-based synthetic traffic generation for web applications using workload patternsProceedings of the 2024 6th Asia Pacific Information Technology Conference10.1145/3651623.3651638(22-30)Online publication date: 29-Jan-2024
https://dl.acm.org/doi/10.1145/3651623.3651638
Zhao GGeorgiou SHassan SZou YTruong DCorbin TSpinellis DConstantinou EBacchelli A(2024)Enhancing Performance Bug Prediction Using Performance Code MetricsProceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644920(50-62)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644920
Khosravi Tabrizi AEzzati-Jivan NTetreault FBalsamo SKnottenbelt WAbad CShang W(2024)An Adaptive Logging System (ALS): Enhancing Software Logging with Reinforcement Learning TechniquesProceedings of the 15th ACM/SPEC International Conference on Performance Engineering10.1145/3629526.3645033(37-47)Online publication date: 7-May-2024
https://dl.acm.org/doi/10.1145/3629526.3645033
Al-Bataineh OMoonen LVidziunas L(2024)Extending the range of bugs that automated program repair can handleJournal of Systems and Software10.1016/j.jss.2023.111918209(111918)Online publication date: Mar-2024
https://doi.org/10.1016/j.jss.2023.111918
Li FLou YTan XChen ZDong JLi YWang XHao DZhang L(2024)What can we learn from quality assurance badges in open-source software?Science China Information Sciences10.1007/s11432-022-3611-367:4Online publication date: 26-Mar-2024
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Timperley Cvan der Hoorn GSantos ADeshpande HWąsowski A(2024)ROBUST: 221 bugs in the Robot Operating SystemEmpirical Software Engineering10.1007/s10664-024-10440-029:3Online publication date: 23-Mar-2024
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Al-Bataineh O(2024)Invariant-based Program RepairFundamental Approaches to Software Engineering10.1007/978-3-031-57259-3_12(255-265)Online publication date: 6-Apr-2024
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Xu JLu KDu ZDing ZLi LWu QPayer MMao BCalandrino JTroncoso C(2023)Silent bugs matterProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620442(3655-3672)Online publication date: 9-Aug-2023
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