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

5 Citations (Scopus)

Abstract

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
PublisherIEEE Computer Society
Pages541-546
Number of pages6
Volume1
ISBN (Print)9781479921430, 9780769549224
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event20th Asia-Pacific Software Engineering Conference, APSEC 2013 - Bangkok, Thailand
Duration: Dec 2 2013Dec 5 2013

Other

Other20th Asia-Pacific Software Engineering Conference, APSEC 2013
CountryThailand
CityBangkok
Period12/2/1312/5/13

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Keywords

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

ASJC Scopus subject areas

  • Software

Cite this

Phannachitta, P., Keung, J., Monden, A., & Matsumoto, K. I. (2013). Improving analogy-based software cost estimation through probabilistic-based similarity measures. In APSEC 2013 - Proceedings of the 20th Asia-Pacific Software Engineering Conference (Vol. 1, pp. 541-546). [6805449] IEEE Computer Society. https://doi.org/10.1109/APSEC.2013.78