Influence of input values on the prediction model error using artificial neural network based on Taguchi's orthogonal array

Abstract

Rapid and accurate assessment of software project development using artificial intelligence tools can be essential for success in the software industry. This article has two objectives: to reduce the magnitude relative error (MRE) value in estimating the effort and cost of software development using the proposed artificial neural network architecture based on the Taguchi method and examine the influence of input variables on the change in relative error value. Clustering and fuzzification methods further mitigate the heterogeneous structure of the different project values of the datasets used.Taguchi method contributes to the reduction of the number of iterations by 99%, which achieves a significant reduction in estimation and value of MRE. By monitoring additional criteria, such as prediction, correlation, and comparing two activation functions, such as sigmoid and radial basis function, the proposed model's correctness, reliability, and stability are confirmed. Significantly better results are expected using the sigmoid activation function and a decrease in the value of the mean (MRE).

Special Issue on International Conference on Innovations in Intelligent Systems and Applications (INISTA 2021)

“…The authors propose Taguchi method based artificial neural network architecture to reduce the magnitude relative error value (MRE) in estimating the effort and cost of software development and examine the influence of input variables on the change in MRE.Also, the correctness of improved model is checked on different datasets. 4 Sanghyun Lee, Bongee Hong, and Woochan Kim, "A free-moving object dataset generator for verifying continuous queries in vessel operations. "To evaluate continuous real-time surveillance monitoring queries (CQs) conducted in marine vessel operations, a dynamic testing dataset of moving target objects is necessary.…”

“…Also, the correctness of improved model is checked on different datasets. 4 Sanghyun Lee, Bongee Hong, and Woochan Kim, "A free-moving object dataset generator for verifying continuous queries in vessel operations. "…”

“…The authors propose Taguchi method based artificial neural network architecture to reduce the magnitude relative error value (MRE) in estimating the effort and cost of software development and examine the influence of input variables on the change in MRE. Also, the correctness of improved model is checked on different datasets 4 …”