Software effort estimation as a classification problem

Ayşe Bakir, Burak Turhan, Ayşe Bener

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)


Software cost estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Software cost estimation, up to now, has been treated as a regression problem. However, in order to prevent over/under estimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and classifies new software projects in one of the effort classes each corresponding to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find the corresponding effort classes. The proposed approach is applied to seven public data sets. Our experimental results show that hit rates obtained for effort estimation are around 90%-100%s. For point estimation, the results are also comparable to those in the literature.

Original languageEnglish
Title of host publication2nd International Workshop on Architectures, Concepts and Technologies for Service Oriented Computing, ACT4SOC 2008 - In Conjunction with the 3rd International Conference on Software and Data Technologies, ICSOFT 2008
Number of pages4
Publication statusPublished - 22 Dec 2008
Externally publishedYes
Event3rd International Conference on Software and Data Technologies, ICSOFT 2008 - Porto, Portugal
Duration: 5 Jul 20088 Jul 2008


Conference3rd International Conference on Software and Data Technologies, ICSOFT 2008


  • Classification
  • Cluster analysis
  • Interval prediction
  • Machine learning
  • Software effort estimation

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