Extracting relative thresholds for source code metrics

@article{Oliveira2014ExtractingRT,
  title={Extracting relative thresholds for source code metrics},
  author={Paloma Oliveira and Marco Tulio Valente and Fernando Paim Lima},
  journal={2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE)},
  year={2014},
  pages={254-263}
}
Establishing credible thresholds is a central challenge for promoting source code metrics as an effective instrument to control the internal quality of software systems. To address this challenge, we propose the concept of relative thresholds for evaluating metrics data following heavy-tailed distributions. The proposed thresholds are relative because they assume that metric thresholds should be followed by most source code entities, but that it is also natural to have a number of entities in… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 32 REFERENCES

Comparative analysis of evolving software systems using the Gini coefficient

R. Vasa, M. Lumpe, P. Branchand, O. Nierstrasz
  • 25th IEEE International Conference on Software Maintenance (ICSM), 2009, pp. 179–188.
  • 2009
VIEW 15 EXCERPTS
HIGHLY INFLUENTIAL

The Qualitas Corpus: A Curated Collection of Java Code for Empirical Studies

  • 2010 Asia Pacific Software Engineering Conference
  • 2010
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Power laws in software

  • ACM Trans. Softw. Eng. Methodol.
  • 2008
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

Understanding the shape of Java software

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Practical software quality metrics aggregation

K. Mordal, N. Anquetil, +3 authors S. Ducasse
  • Software Maintenance and Evolution: Research and Practice, pp. 1–19, 2013.
  • 2013
VIEW 2 EXCERPTS

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