Error Estimation Models Integrating Previous Models and Using Artificial Neural Networks for Embedded Software Development Projects

In an earlier paper, we established 9 models for estimating errors in a new project. In this paper, we integrate the 9 models into 5 by investigating similarities among the models. In addition, we establish a new model using an artificial neural network (ANN). It is becoming increasingly important for software-development corporations to ascertain how to develop software efficiently, whilst guaranteeing delivery time and quality, and keeping development costs low. Estimating the manpower required by new projects and guaranteeing the quality of software are particularly important, because the estimation relates directly to costs while the quality reflects on the reliability of the corporations. In the field of embedded software, development techniques, management techniques, tools, testing techniques, reuse techniques, real-time operating systems and so on, have already been studied. However, there is little research on the relationship between the scale of the development and the number of errors using data accumulated from past projects. Hence, we integrate the previous models and establish a new model using an artificial neural network (ANN). We also compare the accuracy of the ANN model and the regression analysis models. The results of these comparisons indicate that the ANN model is more accurate than any of the 5 integrated models.