Incorporating expert judgment into regression models of software effort estimation

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Author(s)

  • Masateru Tsunoda
  • Akito Monden
  • Jacky Keung
  • Kenichi Matsumoto

Detail(s)

Original languageEnglish
Title of host publicationProceedings - Asia-Pacific Software Engineering Conference, APSEC
PublisherIEEE Computer Society
Pages374-379
Volume1
ISBN (Print)9780769549224
Publication statusPublished - 2012
Externally publishedYes

Publication series

Name
Volume1
ISSN (Print)1530-1362

Conference

Title19th Asia-Pacific Software Engineering Conference, APSEC 2012
PlaceChina
CityHong Kong
Period4 - 7 December 2012

Abstract

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%. © 2012 IEEE.

Research Area(s)

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

Citation Format(s)

Incorporating expert judgment into regression models of software effort estimation. / Tsunoda, Masateru; Monden, Akito; Keung, Jacky; Matsumoto, Kenichi.

Proceedings - Asia-Pacific Software Engineering Conference, APSEC. Vol. 1 IEEE Computer Society, 2012. p. 374-379 6462683.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)