Improving analogy-based software cost estimation through probabilistic-based similarity measures

Passakorn Phannachitta, Jacky Keung, Akito Monden, Ken Ichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


The performance of software cost estimation based on analogy reasoning depends upon the measures that specifying the similarity between software projects. This paper empirically investigates the use of probabilistic-based distance functions to improve the similarity measurement. The probabilistic-based distance functions are considerably more robust, because they collect the implicit correlation between the occurrences of project feature attributes. This information gain enables the constructed estimation model to be more concise and comprehensible. The study compares 6 probabilistic-based distance functions against the commonlyused Euclidian distance. We empirically evaluate the implemented cost estimation model using 5 real-world datasets collected from the PROMISE repository. The result shows a significant improvement in terms of error reduction, that implies an estimation based on probabilistic-based distance functions achieve higher accuracy on average, and the peak performance significantly outperforms the Euclidian distance based on Wilcoxon signed-rank test.

Original languageEnglish
Title of host publicationAPSEC 2013 - Proceedings of the 20th Asia-Pacific Software Engineering Conference
EditorsPornsiri Muenchaisri, Gregg Rothermel
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781479921430
ISBN (Print)9780769549224
Publication statusPublished - Jan 1 2013
Externally publishedYes
Event20th Asia-Pacific Software Engineering Conference, APSEC 2013 - Bangkok, Thailand
Duration: Dec 2 2013Dec 5 2013

Publication series

NameProceedings - Asia-Pacific Software Engineering Conference, APSEC
ISSN (Print)1530-1362


Other20th Asia-Pacific Software Engineering Conference, APSEC 2013


  • Analogy
  • Heterogeneous distance function
  • Machine learning
  • Software cost estimation

ASJC Scopus subject areas

  • Software


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