Factors affecting duration and effort estimation errors in software development projects

2007 ◽  
Vol 49 (8) ◽  
pp. 827-837 ◽  
Author(s):  
Ofer Morgenshtern ◽  
Tzvi Raz ◽  
Dov Dvir
Keyword(s):  
Software Development ◽  
Development Projects ◽  
Effort Estimation ◽  
Estimation Errors ◽  
Factors Affecting

Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

2022 ◽  
pp. 1652-1665
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakashima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Software Development ◽  
Embedded Software ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Development Projects ◽  
Effort Estimation ◽  
Software Projects ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann ◽  
Estimation Models

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

2017 ◽  
Vol 5 (4) ◽  
pp. 19-32 ◽  
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakashima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Software Development ◽  
Embedded Software ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Development Projects ◽  
Effort Estimation ◽  
Software Projects ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann ◽  
Estimation Models

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

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