Proceedings of the International Conference on Promotion of Information Technology (ICPIT 2016)

Software Productivity Estimation by Regression and Na‹ve-Bayes Classifier-An Empirical Research

Authors
Jun Wu, Sisi Gao
Corresponding Author
Jun Wu
Available Online August 2016.
DOI
https://doi.org/10.2991/icpit-16.2016.5How to use a DOI?
Keywords
software productivity estimation, regression analysis, naive-bayes classifier
Abstract
Software cost estimation is now a big concern in software engineering. Although many measurement-based analytical approaches have been proposed, some are focused on producing point estimates rather than interval predictions. The objective of this paper is to investigate the software productivity using linear regression and Naive-Bayes classifier methods. We conduct empirical experiments with 66 historical project data sets from China telecommunication operator and compare the fitting and predictive results of project delivery rate using two approaches respectively. The paper demonstrates that Naive-Bayes classifier is robust enough when predicting the software productivity during the early stage of development.
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Proceedings
International Conference on Promotion of Information Technology (ICPIT 2016)
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-219-0
ISSN
2352-538X
DOI
https://doi.org/10.2991/icpit-16.2016.5How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jun Wu
AU  - Sisi Gao
PY  - 2016/08
DA  - 2016/08
TI  - Software Productivity Estimation by Regression and Na‹ve-Bayes Classifier-An Empirical Research
BT  - International Conference on Promotion of Information Technology (ICPIT 2016)
PB  - Atlantis Press
SP  - 20
EP  - 24
SN  - 2352-538X
UR  - https://doi.org/10.2991/icpit-16.2016.5
DO  - https://doi.org/10.2991/icpit-16.2016.5
ID  - Wu2016/08
ER  -