Article

Predicting Faults from Cached History

Authors:

Thomas Zimmermann,

E. James Whitehead Jr.,

Andreas ZellerAuthors Info & Claims

ICSE '07: Proceedings of the 29th international conference on Software Engineering

Pages 489 - 498

https://doi.org/10.1109/ICSE.2007.66

Published: 24 May 2007 Publication History

Abstract

We analyze the version history of 7 software systems to predict the most fault prone entities and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of several related faults. Therefore, we cache locations that are likely to have faults: starting from the location of a known (fixed) fault, we cache the location itself, any locations changed together with the fault, recently added locations, and recently changed locations. By consulting the cache at the moment a fault is fixed, a developer can detect likely fault-prone locations. This is useful for prioritizing verification and validation resources on the most fault prone files or entities. In our evaluation of seven open source projects with more than 200,000 revisions, the cache selects 10% of the source code files; these files account for 73%-95% of faults-- a significant advance beyond the state of the art.

References

[1]

{1} E. Alpaydin, Introduction to Machine Learning: The MIT Press, 2004.

Digital Library

[2]

{2} B. Behlendorf, C. M. Pilato, G. Stein, K. Fogel, K. Hancock, and B. Collins-Sussman, "Subversion Project Homepage," 2005.

[3]

{3} J. Bevan and E. J. Whitehead, Jr., "Identification of Software Instabilities," Proc. of 2003 Working Conference on Reverse Engineering (WCRE 2003), Victoria, Canada, 2003.

Digital Library

[4]

{4} J. Bevan, E. J. Whitehead, Jr., S. Kim, and M. Godfrey, "Facilitating Software Evolution with Kenyon," Proc. of the 2005 European Software Engineering Conference and 2005 Foundations of Software Engineering (ESEC/FSE 2005), Lisbon, Portugal, 2005.

Digital Library

[5]

{5} D. Cubranic and G. C. Murphy, "Hipikat: Recommending pertinent software development artifacts," Proc. of 25th International Conference on Software Engineering (ICSE), Portland, Oregon, 2003, pp. 408-418.

Digital Library

[6]

{6} V. Dallmeier, P. Weißgerber, and T. Zimmermann, "APFEL: A Preprocessing Framework For Eclipse," http://www.st.cs.unisb.de/softevo/apfel/, 2005.

[7]

{7} M. Fischer, M. Pinzger, and H. Gall, "Populating a Release History Database from Version Control and Bug Tracking Systems," Proc. of 2003 Int'l Conference on Software Maintenance (ICSM'03), 2003, pp. 23-32.

Digital Library

[8]

{8} H. Gall, M. Jazayeri, and J. Krajewski, "CVS Release History Data for Detecting Logical Couplings," Proc. of Sixth International Workshop on Principles of Software Evolution (IWPSE'03), Helsinki, Finland, 2003, pp. 13-23.

Digital Library

[9]

{9} M. W. Godfrey and L. Zou, "Using Origin Analysis to Detect Merging and Splitting of Source Code Entities," IEEE Trans. on Software Engineering, vol. 31, pp. 166-181, 2005.

Digital Library

[10]

{10} T. L. Graves, A. F. Karr, J. S. Marron, and H. Siy, "Predicting Fault Incidence Using Software Change History," IEEE Transactions on Software Engineering, vol. 26, pp. 653-661, 2000.

Digital Library

[11]

{11} A. E. Hassan and R. C. Holt, "The Top Ten List: Dynamic Fault Prediction," Proc. of International Conference on Software Maintenance (ICSM 2005), Budapest, Hungary, 2005, pp. 263-272.

Digital Library

[12]

{12} T. M. Khoshgoftaar and E. B. Allen, "Ordering Fault-Prone Software Modules," Software Quality Journal, vol. 11, pp. 19- 37, 2003.

Digital Library

[13]

{13} T. M. Khoshgoftaar and E. B. Allen, "Predicting the Order of Fault-Prone Modules in Legacy Software," Proc. of The Ninth International Symposium on Software Reliability Engineering, Paderborn, Germany, 1998, pp. 344-353.

Digital Library

[14]

{14} S. Kim, K. Pan, and E. J. Whitehead, Jr., "When Functions Change Their Names: Automatic Detection of Origin Relationships," Proc. of 12th Working Conference on Reverse Engineering (WCRE 2005), Pittsburgh, PA, USA, 2005, pp. 143-152.

Digital Library

[15]

{15} S. Kim, T. Zimmermann, K. Pan, and E. J. Whitehead, Jr., "Automatic Identification of Bug Introducing Changes," Proc. of International Conference on Automated Software Engineering (ASE 2006), Tokyo, Japan, 2006.

Digital Library

[16]

{16} A. J. Ko and B. A. Myers, "A Framework and Methodology for Studying the Causes of Software Errors in Programming Systems," Journal of Visual Languages and Computing, vol. 16, pp. 41-84, 2005.

Digital Library

[17]

{17} A. Mockus and L. G. Votta, "Identifying Reasons for Software Changes Using Historic Databases," Proc. of International Conference on Software Maintenance (ICSM 2000), San Jose, California, USA, 2000, pp. 120-130.

Digital Library

[18]

{18} A. Mockus and D. M. Weiss, "Predicting Risk of Software Changes," Bell Labs Technical Journal, vol. 5, pp. 169-180, 2002.

[19]

{19} N. Nagappan and T. Ball, "Use of Relative Code Churn Measures to Predict System Defect Density," Proc. of 2005 Int'l Conference on Software Engineering (ICSE 2005), Saint Louis, Missouri, USA, 2005, pp. 284-292.

Digital Library

[20]

{20} N. Nagappan, T. Ball, and A. Zeller, "Mining Metrics to Predict Component Failures," Proc. of 2006 Int'l Conference on Software Engineering (ICSE 2006), Shanghai, China, 2006, pp. 452-461.

Digital Library

[21]

{21} T. J. Ostrand, E. J. Weyuker, and R. M. Bell, "Predicting the Location and Number of Faults in Large Software Systems," IEEE Transactions on Software Engineering, vol. 31, pp. 340- 355, 2005.

Digital Library

[22]

{22} J. ¿liwerski, T. Zimmermann, and A. Zeller, "When Do Changes Induce Fixes?," Proc. of Int'l Workshop on Mining Software Repositories (MSR 2005), Saint Louis, Missouri, USA, 2005.

Digital Library

[23]

{23} J. ¿liwerski, T. Zimmermann, and A. Zeller, "HATARI: Raising Risk Awareness. Research Demonstration," Proc. of the 2005 European Software Engineering Conference and 2005 Foundations of Software Engineering (ESEC/FSE 2005), Lisbon, Portugal, 2005, pp. 107-110.

Digital Library

[24]

{24} P. Weißgerber and S. Diehl, "Identifying Refactorings from Source-Code Changes," Proc. of International Conference on Automated Software Engineering (ASE 2006), Tokyo, Japan, 2006, pp. 231-240.

Digital Library

[25]

{25} T. Zimmermann and P. Weißgerber, "Preprocessing CVS Data for Fine-Grained Analysis," Proc. of Proc. Intl. Workshop on Mining Software Repositories (MSR), Edinburgh, Scotland, 2004.

[26]

{26} T. Zimmermann, P. Weißgerber, S. Diehl, and A. Zeller, "Mining Version Histories to Guide Software Changes," IEEE Trans. Software Eng., vol. 31, pp. 429-445, 2005.

Digital Library

Cited By

Oueslati KLaberge GLamothe MKhomh F(2024)Mining Action Rules for Defect Reduction PlanningProceedings of the ACM on Software Engineering10.1145/36608091:FSE(2309-2331)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660809
Wang YMo RZhang Y(2024)Machine Learning-based Models for Predicting Defective PackagesProceedings of the 2024 8th International Conference on Machine Learning and Soft Computing10.1145/3647750.3647755(25-31)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3647750.3647755
Green CJaspan CHodges MLin J(2024)Developer Productivity for Humans, Part 7: Software QualityIEEE Software10.1109/MS.2023.332483041:1(25-30)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/MS.2023.3324830
Show More Cited By

Recommendations

Predicting faults from cached history
ISEC '08: Proceedings of the 1st India software engineering conference

We analyze the version history of 7 software systems to predict the most fault prone entities and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of several related faults. Therefore, we cache locations that ...
On undetectable faults and fault diagnosis

The presence of an undetectable fault u_i may modify the response of a detectable fault d_j to a test set used for fault diagnosis. This may impact the accuracy of fault diagnosis based on the responses of single faults. Many state-of-the-art diagnosis ...
Using Dummy Bridging Faults to Define Reduced Sets of Target Faults

To address the large numbers of bridging faults in a circuit, several approaches have been proposed for the selection of subsets of bridging faults as targets for test generation. A different approach that can be viewed as a fault collapsing method ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '07: Proceedings of the 29th international conference on Software Engineering

May 2007

784 pages

ISBN:0769528287

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

IEEE Computer Society

United States

Publication History

Published: 24 May 2007

Check for updates

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

132
Total Citations
View Citations
1,599
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Oueslati KLaberge GLamothe MKhomh F(2024)Mining Action Rules for Defect Reduction PlanningProceedings of the ACM on Software Engineering10.1145/36608091:FSE(2309-2331)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660809
Wang YMo RZhang Y(2024)Machine Learning-based Models for Predicting Defective PackagesProceedings of the 2024 8th International Conference on Machine Learning and Soft Computing10.1145/3647750.3647755(25-31)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3647750.3647755
Green CJaspan CHodges MLin J(2024)Developer Productivity for Humans, Part 7: Software QualityIEEE Software10.1109/MS.2023.332483041:1(25-30)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/MS.2023.3324830
Reck JBach TStoess JChandra SBlincoe KTonella P(2023)A Multidimensional Analysis of Bug Density in SAP HANAProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3613875(1997-2007)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3613875
Hassan FMeng NWang X(2023)UniLoc: Unified Fault Localization of Continuous Integration FailuresACM Transactions on Software Engineering and Methodology10.1145/359379932:6(1-31)Online publication date: 8-May-2023
https://dl.acm.org/doi/10.1145/3593799
Guo ZLiu SLiu XLai WMa MZhang XNi CYang YLi YChen LZhou GZhou Y(2023)Code-line-level Bugginess Identification: How Far have We Come, and How Far have We Yet to Go?ACM Transactions on Software Engineering and Methodology10.1145/358257232:4(1-55)Online publication date: 27-May-2023
https://dl.acm.org/doi/10.1145/3582572
Jesse KKuhmuench CSawant A(2023)RefactorScore: Evaluating Refactor Prone CodeIEEE Transactions on Software Engineering10.1109/TSE.2023.332461349:11(5008-5026)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TSE.2023.3324613
Openja MMorovati MAn LKhomh FAbidi M(2022)Technical debts and faults in open-source quantum software systemsJournal of Systems and Software10.1016/j.jss.2022.111458193:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.jss.2022.111458
Jo JLee JJaffari AKim E(2021)Fault Localization With Data Flow Information and an Artificial Neural NetworkInternational Journal of Software Innovation10.4018/IJSI.20210701059:3(66-78)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.4018/IJSI.2021070105
Gesi JLi JAhmed ILanubile F(2021)An Empirical Examination of the Impact of Bias on Just-in-time Defect PredictionProceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1145/3475716.3475791(1-12)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3475716.3475791
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents