Incorporating expert judgment into regression models of software effort estimation

Masateru Tsunoda, Akito Monden, Jacky Keung, Kenichi Matsumoto

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

7 Citations (Scopus)


One of the common problems in building an effort estimation model is that not all the effort factors are suitable as predictor variables. As a supplement of missing information in estimation models, this paper explores the project manager's knowledge about the target project. We assume that the experts can judge the target project's productivity level based on his/her own expert knowledge about the project. We also assume that this judgment can be further improved, because using the expert's judgment solely could incur subjective perception. This paper proposes a regression model building/selection method to address this challenge. In the proposed method, a fit dataset for model building is divided into two or three subsets by project productivity, and an estimation model is built on each data subset. The expert judges the productivity level of the target project and selects one of the models to be used. In the experiment, we used three datasets to evaluate the produced effort estimation models. In the experiment, we adjusted the error rate of the judgment and analyzed the relationship between the error rate and the estimation accuracy. As a result, the judgment-incorporating models produced significantly higher estimation accuracy than the conventional linear regression model, where the expert's error rate is less than 37%.

Original languageEnglish
Title of host publicationAPSEC 2012 - Proceedings of the 19th Asia-Pacific Software Engineering Conference
PublisherIEEE Computer Society
Number of pages6
ISBN (Print)9780769549224
Publication statusPublished - 2012
Externally publishedYes
Event19th Asia-Pacific Software Engineering Conference, APSEC 2012 - Hong Kong, China
Duration: Dec 4 2012Dec 7 2012

Publication series

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


Other19th Asia-Pacific Software Engineering Conference, APSEC 2012
CityHong Kong


  • Estimation error
  • Expert Judgment
  • Productivity
  • Project Management
  • Software Effort Estimation
  • Stratification

ASJC Scopus subject areas

  • Software


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