Handling categorical variables in effort estimation

Masateru Tsunoda, Sousuke Amasaki, Akito Monden

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)


Background: Accurate effort estimation is the basis of the software development project management. The linear regression model is one of the widely-used methods for the purpose. A dataset used to build a model often includes categorical variables denoting such as programming languages. Categorical variables are usually handled with two methods: the stratification and dummy variables. Those methods have a positive effect on accuracy but have shortcomings. The other handing method, the interaction and the hierarchical linear model (HLM), might be able to compensate for them. However, the two methods have not been examined in the research area. Aim: giving useful suggestions for handling categorical variables with the stratification, transforming dummy variables, the interaction, or HLM, when building an estimation model. Method: We built estimation models with the four handling methods on ISBSG, NASA, and Desharnais datasets, and compared accuracy of the methods with each other. Results: The most effective method was different for datasets, and the difference was statistically significant on both mean balanced relative error (MBRE) and mean magnitude of relative error (MMRE). The interaction and HLM were effective in a certain case. Conclusions: The stratification and transforming dummy variables should be tried at least, for obtaining an accurate model. In addition, we suggest that the application of the interaction and HLM should be considered when building the estimation model.

Original languageEnglish
Pages (from-to)99-102
Number of pages4
JournalInternational Symposium on Empirical Software Engineering and Measurement
Publication statusPublished - 2012
Externally publishedYes
Event6th ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2012 - Lund, Sweden
Duration: Sept 19 2012Sept 20 2012


  • Dummy variable
  • Hierarchical linear model
  • Interaction
  • Mixed effects
  • Model-based effort estimation
  • Stratification

ASJC Scopus subject areas

  • Computer Science Applications
  • Software


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