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

In this paper we propose a method for reducing the margin of error in effort and error prediction models for embedded software development projects using artificial neural networks(ANNs). In addition, we perform an evaluation experiment that uses Welch’s t-test to compare the accuracy of the proposed ANN method with that of our original ANN model. The results show that the proposed ANN model is more accurate than the original one in predicting the number of errors for new projects, since the means of the errors in the proposed ANN are statistically significantly lower.

[1]  Naohiro Ishii,et al.  Using an Artificial Neural Network for Predicting Embedded Software Development Effort , 2009, 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing.

[2]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[3]  Naohiro Ishii,et al.  Error Estimation Models Integrating Previous Models and Using Artificial Neural Networks for Embedded Software Development Projects , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[4]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .