Welcome to the InfoSci Platform
Software Cost Estimation: A State-of-the-Art Statistical and Visualization Approach for Missing Data

Software Cost Estimation: A State-of-the-Art Statistical and Visualization Approach for Missing Data

Panagiota Chatzipetrou
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 18
ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781522565840|DOI: 10.4018/IJSSMET.2019070102
Cite Article Cite Article

MLA

Chatzipetrou, Panagiota. "Software Cost Estimation: A State-of-the-Art Statistical and Visualization Approach for Missing Data." IJSSMET vol.10, no.3 2019: pp.14-31. http://doi.org/10.4018/IJSSMET.2019070102

APA

Chatzipetrou, P. (2019). Software Cost Estimation: A State-of-the-Art Statistical and Visualization Approach for Missing Data. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 10(3), 14-31. http://doi.org/10.4018/IJSSMET.2019070102

Chicago

Chatzipetrou, Panagiota. "Software Cost Estimation: A State-of-the-Art Statistical and Visualization Approach for Missing Data," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 10, no.3: 14-31. http://doi.org/10.4018/IJSSMET.2019070102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation, expectation maximization and regression imputation; and compared with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error. The comparisons are conducted using statistical tests, resampling techniques and visualization tools like the regression error characteristic curves.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.