Improving Accuracy of an Artificial Neural Network Model to Predict Effort and Errors in Embedded Software Development Projects

Iwata, Kazunori; Nakashima, Toyoshiro; Anan, Yoshiyuki; Ishii, Naohiro

doi:10.1007/978-3-642-13265-0_2

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2012

2024

Publication Types

Select...

Book5

Article1

Relationship

Self Cite1

Independent5

Authors

Journals

Cited by 12 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In earlier papers, we showed that ANN models are superior to regression analysis models for predicting effort and errors in new projects. In addition, we proposed a method for reducing this margin of error (Iwata, Nakashima, Anan, & Ishii, 2010). However, methods using ANNs have reached the limit in their improvement, because these methods estimate an appropriate value using what is known as point estimation in statistics.…”

Section: Introductionmentioning

confidence: 99%

Estimating Interval of the Number of Errors for Embedded Software Development Projects

Iwata

Nakasima

Anan

et al. 2014

International Journal of Software Innovation

Self Cite

View full text Add to dashboard Cite

Previous investigation focused on the prediction of total and errors for embedded software development projects using an artificial neural network (ANN). However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. This paper proposes a method for predicting the number of errors for embedded software development projects using interval estimation provided by a support vector machine and ANN.

show abstract

Section: Introductionmentioning

confidence: 99%