Software Cost Estimation

2019 ◽  
Vol 10 (3) ◽  
pp. 14-31
Author(s):  
Panagiota Chatzipetrou
Keyword(s):  
Missing Data ◽  
Cost Estimation ◽  
Multinomial Logistic Regression ◽  
Cost Model ◽  
Statistical Tests ◽  
Error Characteristic ◽  
Cost Models ◽  
Least Squares Regression ◽  
Software Cost Estimation ◽  
Software Cost

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.

Methods for Statistical and Visual Comparison of Imputation Methods for Missing Data in Software Cost Estimation

2011 ◽  
pp. 221-241
Author(s):  
Lefteris Angelis ◽  
Panagiotis Sentas ◽  
Nikolaos Mittas ◽  
Panagiota Chatzipetrou
Keyword(s):  
Missing Data ◽  
Cost Estimation ◽  
Cost Model ◽  
Statistical Tests ◽  
Computational Techniques ◽  
Software Project ◽  
Software Cost Estimation ◽  
Software Cost ◽  
Regression Imputation ◽  
Mean Imputation

Software Cost Estimation is a critical phase in the development of a software project, and over the years has become an emerging research area. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. The purpose of this chapter is to show how a combination of modern statistical and computational techniques can be used to compare the effect of missing data techniques on the accuracy of cost estimation. Specifically, a recently proposed missing data technique, the multinomial logistic regression, is evaluated and compared with four older methods: listwise deletion, mean imputation, expectation maximization and regression imputation 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 and the comparisons are conducted using statistical tests, resampling techniques and a visualization tool, the regression error characteristic curves.

A Framework of Statistical and Visualization Techniques for Missing Data Analysis in Software Cost Estimation

2018 ◽  
pp. 345-372
Author(s):  
Lefteris Angelis ◽  
Nikolaos Mittas ◽  
Panagiota Chatzipetrou
Keyword(s):  
Missing Data ◽  
Cost Estimation ◽  
Prediction Models ◽  
Software Cost Estimation ◽  
Software Cost ◽  
Regression Imputation ◽  
Mean Imputation ◽  
Regression Error ◽  
Visualization Techniques ◽  
Missing Data Techniques

Software Cost Estimation (SCE) is a critical phase in software development projects. However, due to the growing complexity of the software itself, a common problem in building software cost models is that the available datasets contain lots of missing categorical data. The purpose of this chapter is to show how a framework of statistical, computational, and visualization techniques can be used to evaluate and compare the effect of missing data techniques on the accuracy of cost estimation models. Hence, the authors use five missing data techniques: Multinomial Logistic Regression, Listwise Deletion, Mean Imputation, Expectation Maximization, and Regression Imputation. The evaluation and the comparisons are conducted using Regression Error Characteristic curves, which provide visual comparison of different prediction models, and Regression Error Operating Curves, which examine predictive power of models with respect to under- or over-estimation.

A Framework of Statistical and Visualization Techniques for Missing Data Analysis in Software Cost Estimation

2015 ◽  
pp. 71-97
Author(s):  
Lefteris Angelis ◽  
Nikolaos Mittas ◽  
Panagiota Chatzipetrou
Keyword(s):  
Missing Data ◽  
Cost Estimation ◽  
Prediction Models ◽  
Software Cost Estimation ◽  
Software Cost ◽  
Regression Imputation ◽  
Mean Imputation ◽  
Regression Error ◽  
Visualization Techniques ◽  
Missing Data Techniques

Software Cost Estimation (SCE) is a critical phase in software development projects. However, due to the growing complexity of the software itself, a common problem in building software cost models is that the available datasets contain lots of missing categorical data. The purpose of this chapter is to show how a framework of statistical, computational, and visualization techniques can be used to evaluate and compare the effect of missing data techniques on the accuracy of cost estimation models. Hence, the authors use five missing data techniques: Multinomial Logistic Regression, Listwise Deletion, Mean Imputation, Expectation Maximization, and Regression Imputation. The evaluation and the comparisons are conducted using Regression Error Characteristic curves, which provide visual comparison of different prediction models, and Regression Error Operating Curves, which examine predictive power of models with respect to under- or over-estimation.

A Framework of Statistical and Visualization Techniques for Missing Data Analysis in Software Cost Estimation

2017 ◽  
pp. 433-460
Author(s):  
Lefteris Angelis ◽  
Nikolaos Mittas ◽  
Panagiota Chatzipetrou
Keyword(s):  
Missing Data ◽  
Cost Estimation ◽  
Prediction Models ◽  
Software Cost Estimation ◽  
Software Cost ◽  
Regression Imputation ◽  
Mean Imputation ◽  
Regression Error ◽  
Visualization Techniques ◽  
Missing Data Techniques

Software Cost Estimation (SCE) is a critical phase in software development projects. However, due to the growing complexity of the software itself, a common problem in building software cost models is that the available datasets contain lots of missing categorical data. The purpose of this chapter is to show how a framework of statistical, computational, and visualization techniques can be used to evaluate and compare the effect of missing data techniques on the accuracy of cost estimation models. Hence, the authors use five missing data techniques: Multinomial Logistic Regression, Listwise Deletion, Mean Imputation, Expectation Maximization, and Regression Imputation. The evaluation and the comparisons are conducted using Regression Error Characteristic curves, which provide visual comparison of different prediction models, and Regression Error Operating Curves, which examine predictive power of models with respect to under- or over-estimation.

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