Improving expert prediction of issue resolution time

@inproceedings{Pfahl2016ImprovingEP,
  title={Improving expert prediction of issue resolution time},
  author={Dietmar Pfahl and Siim Karus and Myroslava Stavnycha},
  booktitle={EASE},
  year={2016}
}
Predicting the resolution times of issue reports in software development is important, because it helps allocate resources adequately. However, issue resolution time (IRT) prediction is difficult and prediction quality is limited. A common approach in industry is to base predictions on expert knowledge. While this manual approach requires the availability and effort of experts, automated approaches using data mining and machine learning techniques require a small upfront investment for setting… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-5 of 5 references

Chapter 4: Ordinal Regression

M. J. Norušis
PASW Statistics 18.0 Advanced Statistical Procedures Companion, 2010, p. 648. • 2010
View 11 Excerpts
Highly Influenced

How Long Will It Take to Fix This Bug?

Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007) • 2007
View 4 Excerpts
Highly Influenced

Bug fix-time prediction model using naïve Bayes classifier

2012 22nd International Conference on Computer Theory and Applications (ICCTA) • 2012
View 3 Excerpts
Highly Influenced

Filtering Bug Reports for Fix-Time Analysis

2012 16th European Conference on Software Maintenance and Reengineering • 2012
View 3 Excerpts
Highly Influenced