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Locating where faults will be

Authors:

Thomas J. Ostrand,

Elaine J. Weyuker,

Robert M. BellAuthors Info & Claims

TAPIA '05: Proceedings of the 2005 conference on Diversity in computing

Pages 48 - 50

https://doi.org/10.1145/1095242.1095262

Published: 19 October 2005 Publication History

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References

[1]

T. Ostrand and E.J. Weyuker. The Distribution of Faults in a Large Industrial Software System. Proc. ACM/International Symposium on Software Testing and Analysis (ISSTA2002), Rome, Italy, July 2002, pp.55--64.

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[2]

T. Ostrand, E.J. Weyuker, and R. Bell. Predicting the Location and Number of Faults in Large Software Systems. IEEE Trans. on Software Engineering, Vol 31, No 4, April 2005.

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[3]

T. Ostrand, E.J. Weyuker, and R. Bell. A Different View of Fault Prediction Proc. 29th IEEE Annual International Computer Software and Applications Conference (COMPSAC 2005), Edinburgh, Scotland, July, 2005.

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Hall TBeecham SBowes DGray DCounsell S(2012)A Systematic Literature Review on Fault Prediction Performance in Software EngineeringIEEE Transactions on Software Engineering10.1109/TSE.2011.10338:6(1276-1304)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1109/TSE.2011.103
Meneely AWilliams LSnipes WOsborne JHarrold MMurphy G(2008)Predicting failures with developer networks and social network analysisProceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering10.1145/1453101.1453106(13-23)Online publication date: 9-Nov-2008
https://dl.acm.org/doi/10.1145/1453101.1453106

Index Terms

Locating where faults will be
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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The ability to predict which files in a large software system are most likely to contain the largest numbers of faults in the next release can be a very valuable asset. To accomplish this, a negative binomial regression model using information from ...
Where the bugs are

The ability to predict which files in a large software system are most likely to contain the largest numbers of faults in the next release can be a very valuable asset. To accomplish this, a negative binomial regression model using information from ...
Predicting the Location and Number of Faults in Large Software Systems

Advance knowledge of which files in the next release of a large software system are most likely to contain the largest numbers of faults can be a very valuable asset. To accomplish this, a negative binomial regression model has been developed and used ...

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TAPIA '05: Proceedings of the 2005 conference on Diversity in computing

October 2005

76 pages

ISBN:1595932577

DOI:10.1145/1095242

General Chair:
Pamela J. Williams
Sandia National Laboratories, USA
,
Program Chair:
Mark A. Friedman
The Richard J. Roman Institute, USA

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

New York, NY, United States

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Published: 19 October 2005

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TAPIA05: Richard Tapia Celebration of Diversity in Computing Conference

October 19 - 22, 2005

New Mexico, Albuquerque, USA

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Hall TBeecham SBowes DGray DCounsell S(2012)A Systematic Literature Review on Fault Prediction Performance in Software EngineeringIEEE Transactions on Software Engineering10.1109/TSE.2011.10338:6(1276-1304)Online publication date: 1-Nov-2012
https://dl.acm.org/doi/10.1109/TSE.2011.103
Meneely AWilliams LSnipes WOsborne JHarrold MMurphy G(2008)Predicting failures with developer networks and social network analysisProceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering10.1145/1453101.1453106(13-23)Online publication date: 9-Nov-2008
https://dl.acm.org/doi/10.1145/1453101.1453106

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Where the bugs are

Predicting the Location and Number of Faults in Large Software Systems

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Predicting the Location and Number of Faults in Large Software Systems

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