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MIMIC: locating and understanding bugs by analyzing mimicked executions

Authors:

Daniele Zuddas,

Fabrizio Pastore,

Leonardo Mariani,

Alessandro OrsoAuthors Info & Claims

ASE '14: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

Pages 815 - 826

https://doi.org/10.1145/2642937.2643014

Published: 15 September 2014 Publication History

Abstract

Automated debugging techniques aim to help developers locate and understand the cause of a failure, an extremely challenging yet fundamental task. Most state-of-the-art approaches suffer from two problems: they require a large number of passing and failing tests and report possible faulty code with no explanation. To mitigate these issues, we present MIMIC, a novel automated debugging technique that combines and extends our previous input generation and anomaly detection techniques. MIMIC (1) synthesizes multiple passing and failing executions similar to an observed failure and (2) uses these executions to detect anomalies in behavior that may explain the failure. We evaluated MIMIC on six failures of real-world programs with promising results: for five of these failures, MIMIC identified their root causes while producing a limited number of false positives. Most importantly, the anomalies identified by MIMIC provided information that may help developers understand (and ultimately eliminate) such root causes.

References

[1]

Coreutils. http://www.gnu.org/software/coreutils/.

[2]

R. Abreu, P. Zoeteweij, and A. J. C. v. Gemund. An Evaluation of Similarity Coefficients for Software Fault Localization. In Proceedings of the Pacific Rim International Symposium on Dependable Computing, pages 39--46, 2006.

Digital Library

[3]

S. Artzi, J. Dolby, F. Tip, and M. Pistoia. Practical Fault Localization for Dynamic Web Applications. In Proceedings of the International Conference on Software Engineering, pages 265--274. ACM, 2010.

Digital Library

[4]

A. Babenko, L. Mariani, and F. Pastore. AVA: Automated Interpretation of Dynamically Detected Anomalies. In Proceedings of the International Symposium on Software Testing and Analysis, pages 237--248, 2009.

Digital Library

[5]

L. Briand, Y. Labiche, and X. Liu. Using Machine Learning to Support Debugging with Tarantula. In Proceedings of the International Symposium on Software Reliability Engineering, pages 137--146, 2007.

Digital Library

[6]

CoreUtils. Fault in Mknod. http://lists.gnu.org/archive/html/bug-coreutils/2008-03/msg00224.html.

[7]

CoreUtils. Fault in OD. http://lists.gnu.org/archive/html/bug-coreutils/2007-08/msg00034.html.

[8]

CoreUtils. Fault in PR. http://lists.gnu.org/archive/html/bug-coreutils/2008-04/msg00177.html.

[9]

C. Csallner and Y. Smaragdakis. Dynamically Discovering Likely Interface Invariants. In Proceedings of the International Conference on Software Engineering, pages 861--864, 2006.

Digital Library

[10]

C. Csallner and Y. Smaragdakis. DSD-Crasher: A hybrid analysis tool for bug finding. ACM Transactions on Software Engineering and Methodologie, 17(2):245--254, 2008.

Digital Library

[11]

Davide Libenzi. XMail. http://www.xmailserver.org/.

[12]

M. Dimitrov and H. Zhou. Anomaly-based Bug Prediction, Isolation, and Validation: An Automated Approach for Software Debugging. In Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems, pages 61--72, 2009.

Digital Library

[13]

M. D. Ernst, J. Cockrell, W. G. Griswold, and D. Notkin. Dynamically Discovering Likely Program Invariants to Support Program Evolution. IEEE Transactions on Software Engineering, 27(2):99--123, 2001.

Digital Library

[14]

G. Fraser and A. Arcuri. EvoSuite: Automatic Test Suite Generation for Object-oriented Software. In Proceedings of the ACM Symposium and the European Conference on Foundations of Software Engineering, pages 416--419. ACM, 2011.

Digital Library

[15]

W. Jin and A. Orso. Bugredux: Reproducing field failures for in-house debugging. In Proceedings of the International Conference on Software Engineering, pages 474--484, 2012.

Digital Library

[16]

W. Jin and A. Orso. F3: fault localization for field failures. In Proceedings of the International Symposium on Software Testing and Analysis, pages 213--223, 2013.

Digital Library

[17]

J. Jones, M. J. Harrold, and J. Stasko. Visualization of test information to assist fault localization. In Proceedings of the International Conference on Software Engineering, pages 467--477, 2002.

Digital Library

[18]

F. M. Kifetew, W. Jin, R. Tiella, A. Orso, and P. Tonella. Reproducing Field Failures for Programs with Complex Grammar-Based Input. In Proceedings of the IEEE International Conference on Software Testing, Verification and Validation, pages 163--172, March 2014.

Digital Library

[19]

B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. I. Jordan. Scalable Statistical Bug Isolation. In Proceedings of the Conference on Programming Language Design and Implementation, pages 15--26, 2005.

Digital Library

[20]

Lincoln University of Nebraska. Software-artifact Infrastructure Repository. http://sir.unl.edu/.

[21]

C. Liu, X. Yan, L. Fei, J. Han, and S. P. Midkiff. SOBER: Statistical Model-based Bug Localization. In Proceedings of the ACM Symposium and the European Conference on Foundations of Software Engineering, pages 286--295, 2005.

Digital Library

[22]

L. Mariani and F. Pastore. Automated Identification of Failure Causes in System Logs. In Proceedings of the International Symposium on Software Reliability Engineering, pages 117--126, 2008.

Digital Library

[23]

L. Mariani, F. Pastore, and M. Pezze. Dynamic Analysis for Diagnosing Integration Faults. IEEE Transactions on Software Engineering, 37(4):486--508, 2011.

Digital Library

[24]

C. Parnin and A. Orso. Are automated debugging techniques actually helping programmers? In Proceedings of the International Symposium on Software Testing and Analysis, pages 199--209, July 2011.

Digital Library

[25]

F. Pastore, L. Mariani, and A. Goffi. RADAR: A tool for debugging regression problems in C/C++ software. In Proceedings of the International Conference on Software Engineering, pages 1335--1338, 2013.

Digital Library

[26]

F. Pastore, L. Mariani, A. Goffi, M. Oriol, and M. Wahler. Dynamic analysis of upgrades in C/C++ software. In International Symposium on Software Reliability Engineering, pages 91--100, 2012.

Digital Library

[27]

M. Renieris and S. Reiss. Fault Localization with Nearest Neighbor Queries. In Proceedings of the International Conference on Automated Software Engineering, pages 30--39, 2003.

[28]

J. Rößler, G. Fraser, A. Zeller, and A. Orso. Isolating Failure Causes Through Test Case Generation. In Proceedings of International Symposium on Software Testing and Analysis, pages 309--319, 2012.

Digital Library

[29]

S. K. Sahoo, J. Criswell, C. Geigle, and V. Adve. Using Likely Invariants for Automated Software Fault Localization. In Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems, pages 139--152, 2013.

Digital Library

[30]

A. Wasylkowski, A. Zeller, and C. Lindig. Detecting Object Usage Anomalies. In Proceedings of the ACM Symposium and the European Conference on Foundations of Software Engineering, pages 35--44, 2007.

Digital Library

[31]

T. Xie and D. Notkin. Tool-assisted Unit Test Selection based on Operational Violations. In Proceedings of the International Conference on Automated Software Engineering, pages 40--48, 2003.

[32]

Y. Yu, J. Jones, and M. J. Harrold. An Empirical Study of the Effects of Test-suite Reduction on Fault Localization. In Proceedings of the International Conference on Software Engineering, pages 201--210. ACM, 2008.

Digital Library

[33]

A. Zeller and R. Hildebrandt. Simplifying and Isolating Failure-Inducing Input. IEEE Transactions on Software Engineering, 28(2):183--200, 2002.

Digital Library

[34]

X. Zhang, N. Gupta, and R. Gupta. Locating Faults Through Automated Predicate Switching. In Proceedings of the International Conference on Software Engineering, pages 272--281, 2006.

Digital Library

Cited By

Mobilio MRiganelli OMicucci DMariani L(2024)FILO: Automated FIx-LOcus Identification for Android Framework Compatibility IssuesInformation10.3390/info1508042315:8(423)Online publication date: 23-Jul-2024
https://doi.org/10.3390/info15080423
Bartocci EMariani LNickovic DYadav D(2022)Search-based Testing for Accurate Fault Localization in CPS2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE55969.2022.00024(145-156)Online publication date: Oct-2022
https://doi.org/10.1109/ISSRE55969.2022.00024
Zuo GMa JQuinn ABhatotia PFonseca PKasikci BFreund SYahav E(2021)Execution reconstruction: harnessing failure reoccurrences for failure reproductionProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454101(1155-1170)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454101
Show More Cited By

Index Terms

MIMIC: locating and understanding bugs by analyzing mimicked executions
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Published In

cover image ACM Conferences

ASE '14: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

September 2014

934 pages

ISBN:9781450330138

DOI:10.1145/2642937

General Chair:
Ivica Crnkovic
Mälardalen University, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Paul Grünbacher
Johannes Kepler Universität Linz, Austria

Copyright © 2014 ACM.

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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SIGAI: ACM Special Interest Group on Artificial Intelligence
SIGSOFT: ACM Special Interest Group on Software Engineering
Mälardalen University: Mälardalen University
IEEE CS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 September 2014

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ASE '14

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SIGAI
SIGSOFT
Mälardalen University

ASE '14: ACM/IEEE International Conference on Automated Software Engineering

September 15 - 19, 2014

Vasteras, Sweden

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ASE '14 Paper Acceptance Rate 82 of 337 submissions, 24%;

Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

Mobilio MRiganelli OMicucci DMariani L(2024)FILO: Automated FIx-LOcus Identification for Android Framework Compatibility IssuesInformation10.3390/info1508042315:8(423)Online publication date: 23-Jul-2024
https://doi.org/10.3390/info15080423
Bartocci EMariani LNickovic DYadav D(2022)Search-based Testing for Accurate Fault Localization in CPS2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE55969.2022.00024(145-156)Online publication date: Oct-2022
https://doi.org/10.1109/ISSRE55969.2022.00024
Zuo GMa JQuinn ABhatotia PFonseca PKasikci BFreund SYahav E(2021)Execution reconstruction: harnessing failure reoccurrences for failure reproductionProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454101(1155-1170)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454101
Johnson BBrun YMeliou ARothermel GBae D(2020)Causal testingProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380377(87-99)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380377
Zhang LLi ZFeng YZhang ZChan WZhang JZhou Y(2020)Improving Fault-Localization Accuracy by Referencing Debugging History to Alleviate Structure Bias in Code SuspiciousnessIEEE Transactions on Reliability10.1109/TR.2020.298297569:3(1021-1049)Online publication date: Sep-2020
https://doi.org/10.1109/TR.2020.2982975
Gazzola LMicucci DMariani L(2019)Automatic Software RepairIEEE Transactions on Software Engineering10.1109/TSE.2017.275501345:1(34-67)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TSE.2017.2755013
Mobilio MRiganelli OMicucci DMariani L(2019)FILO: FIx-LOcus Recommendation for Problems Caused by Android Framework Upgrade2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE.2019.00043(358-368)Online publication date: Oct-2019
https://doi.org/10.1109/ISSRE.2019.00043
Naitou KTanikado AMatsumoto SHigo YKusumoto SKirinuki HKurabayashi TTanno HKhomh FRoy CSiegmund J(2018)Toward introducing automated program repair techniques to industrial software developmentProceedings of the 26th Conference on Program Comprehension10.1145/3196321.3196358(332-335)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3196321.3196358
Ang APerez AVan Deursen AAbreu R(2017)Revisiting the Practical Use of Automated Software Fault Localization Techniques2017 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)10.1109/ISSREW.2017.68(175-182)Online publication date: Oct-2017
https://doi.org/10.1109/ISSREW.2017.68
Saha RGligoric M(2017)Selective Bisection DebuggingProceedings of the 20th International Conference on Fundamental Approaches to Software Engineering - Volume 1020210.1007/978-3-662-54494-5_4(60-77)Online publication date: 22-Apr-2017
https://dl.acm.org/doi/10.1007/978-3-662-54494-5_4
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