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Incorporating Expert Judgment into Regression Models of Software Effort Estimation

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dc.contributor.author Tsunoda, Masateru
dc.contributor.author Monden, Akito
dc.contributor.author Keung, Jacky
dc.contributor.author Matsumoto, Kenichi
dc.date.accessioned 2018-10-30T04:57:15Z
dc.date.available 2018-10-30T04:57:15Z
dc.date.issued 2012
dc.identifier.isbn 9781467349307
dc.identifier.issn 1530-1362
dc.identifier.uri http://hdl.handle.net/10061/12747
dc.description 2012 19th Asia-Pacific Software Engineering Conference, 4-7 Dec. 2012, Hong Kong, China
dc.description.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%.
dc.language.iso en
dc.publisher IEEE
dc.rights c Copyright IEEE 2012
dc.subject regression analysis
dc.subject software cost estimation
dc.subject software development management
dc.subject expert judgment
dc.subject software effort estimation
dc.subject predictor variable
dc.subject project productivity
dc.subject subjective perception
dc.subject regression model building method
dc.subject regression model selection method
dc.subject linear regression model
dc.subject judgment-incorporating model
dc.subject project manager
dc.subject project productivity level
dc.subject Software engineering
dc.subject Software Effort Estimation
dc.subject Project Management
dc.subject Expert Judgment
dc.subject Stratification
dc.subject Productivity
dc.subject Estimation error
dc.title Incorporating Expert Judgment into Regression Models of Software Effort Estimation
dc.type.nii Conference Paper
dc.identifier.fulltexturl https://doi.org/10.1109/APSEC.2012.58
dc.textversion author
dc.identifier.spage 374
dc.identifier.epage 379
dc.relation.doi 10.1109/APSEC.2012.58
dc.identifier.NAIST-ID 73292310


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