Entropy based bug prediction using support vector regression

@article{Singh2012EntropyBB,
  title={Entropy based bug prediction using support vector regression},
  author={V. B. Singh and K. K. Chaturvedi},
  journal={2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)},
  year={2012},
  pages={746-751}
}
Predicting software defects is one of the key areas of research in software engineering. Researchers have devised and implemented a plethora of defect/bug prediction approaches namely code churn, past bugs, refactoring, number of authors, file size and age, etc by measuring the performance in terms of accuracy and complexity. Different mathematical models have also been developed in the literature to monitor the bug occurrence and fixing process. These existing mathematical models named… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 28 REFERENCES

Predicting Faults based on complexity of code change

A. E. Hassan
  • In the proceedings of 31st Intl. Conf. on Software Engineering
  • 2009
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

The Nature of Statistical Learning Theory

  • Statistics for Engineering and Information Science
  • 1995
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

An extensive comparison of bug prediction approaches

  • 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010)
  • 2010
VIEW 1 EXCERPT

A Study on Software Reliability Growth Modeling using Change Point and Fault Dependency

V. B. Singh
  • 2008

Towards a Theoretical Model for Software Growth

  • Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007)
  • 2007
VIEW 1 EXCERPT