Using an Artificial Neural Network for Predicting Embedded Software Development Effort

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

doi:10.1109/snpd.2009.49

Cited by 14 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third category of prediction methods are supervised algorithms for fault prediction, which are generally divided into two groups: regression and classification. These methods are shown in Figure 10 35,141–160 …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Software reliability prediction: A survey

Oveisi

Moeini

Mirzaei

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

Softwares play an important role in controlling complex systems. Monitoring the proper functioning of the components of such systems is the principal role of softwares. Often, a petite fault in one of the subsystems may cause irreparable damages; therefore, it is of great importance to be able to predict software faults and estimate the reliability of softwares. In this survey, we present a classification of various methods proposed in the literature to predict software reliability. This study summarizes the results of more than 200 research papers in the field. We also discuss the challenges involved in prediction methods along with proposed partial solutions (i.e., Bayesian methods) to improve the accuracy of such predictions. Moreover, we review numerous evaluation measures introduced so far to assess the performance of prediction models, the datasets they are based on, and also the results they yield.

show abstract

Section: Methodsmentioning

confidence: 99%

“…These methods are shown in Figure 10. 35,[141][142][143][144][145][146][147][148][149][150][151][152][153][154][155][156][157][158][159][160] F I G U R E 9 Fuzzy logic system for fault prediction F I G U R E 1 0 Supervised algorithms for prediction…”

Section: 14mentioning

confidence: 99%

Software reliability prediction: A survey

Oveisi

Moeini

Mirzaei

et al. 2022

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Previously, we investigated the prediction of total effort and errors using an artificial neural network (ANN) (Iwata, Nakashima, Anan, & Ishii, 2008;Iwata, Anan, Nakashima, & Ishii, 2009;Iwata, Nakashima, Anan, & Ishii, 2012, 2013. In earlier papers, we showed that ANN models are superior to regression analysis models for predicting effort and errors in new projects.…”

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