Abstract
To construct a better multivariate regression model for software effort estimation, this paper proposes a method to select projects as a fit data from a given project data set based on estimation target's features. While regression models were often constructed from all available project data, this paper showed the necessity of fit data selection, and showed that the proposed method is one of the effective and systematic means to do the selection.
| Original language | English |
|---|---|
| Title of host publication | ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement |
| Pages | 360-361 |
| Number of pages | 2 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 - Kaiserslautern, Germany Duration: Oct 9 2008 → Oct 10 2008 |
Other
| Other | 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008 |
|---|---|
| Country | Germany |
| City | Kaiserslautern |
| Period | 10/9/08 → 10/10/08 |
Keywords
- Effort estimation
- Multivariate regression
ASJC Scopus subject areas
- Computer Science Applications
- Software
- Electrical and Electronic Engineering
Cite this
Fit data selection for software effort estimation models. / Toda, Koji; Monden, Akito; Matsumoto, Ken Ichi.
ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. 2008. p. 360-361.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Fit data selection for software effort estimation models
AU - Toda, Koji
AU - Monden, Akito
AU - Matsumoto, Ken Ichi
PY - 2008
Y1 - 2008
N2 - To construct a better multivariate regression model for software effort estimation, this paper proposes a method to select projects as a fit data from a given project data set based on estimation target's features. While regression models were often constructed from all available project data, this paper showed the necessity of fit data selection, and showed that the proposed method is one of the effective and systematic means to do the selection.
AB - To construct a better multivariate regression model for software effort estimation, this paper proposes a method to select projects as a fit data from a given project data set based on estimation target's features. While regression models were often constructed from all available project data, this paper showed the necessity of fit data selection, and showed that the proposed method is one of the effective and systematic means to do the selection.
KW - Effort estimation
KW - Multivariate regression
UR - http://www.scopus.com/inward/record.url?scp=62949229144&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62949229144&partnerID=8YFLogxK
U2 - 10.1145/1414004.1414084
DO - 10.1145/1414004.1414084
M3 - Conference contribution
AN - SCOPUS:62949229144
SN - 9781595939715
SP - 360
EP - 361
BT - ESEM'08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
ER -