Yoshiyuki Anan

Using an Artificial Neural Network for Predicting Embedded Software Development Effort

In this paper, we establish an effort prediction modelusing an artificial neural network (ANN) for complementingmissing values. We add missing values to the data viacollaborative filtering using the method of Tsunoda et al.’smethod[14]. In addition, we perform an evaluation experimentto compare the accuracy of the ANN model with that ofthe MRA model using Welch’s t-test[16]. The results showthat the ANN model is more accurate than the MRA model,since the mean errors of the ANN are statistically significantlylower.

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.

Improving Accuracy of Multiple Regression Analysis for Effort Prediction Model

In this paper, we outline the effort prediction model and the evaluation experiment. In addition we explore the parameters in the model. The model predicts effort of embedded software developments via multiple regression analysis using the collaborative filtering. Because companies, recently, focus on methods to predict effort of projects, which prevent project failures such as exceeding deadline and cost, due to more complex embedded software, which brings the evolution of the performance and function enhancement. In the model, we have fixed two parameters named k and ampmax, which would influence the accuracy of predicting effort. Hence, we investigate a tendency of them in the model and find the optimum value

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.

Studies on Project Management Models for Embedded Software Development Projects

In a corporation or the division of a corporation where software is being developed, it is becoming very important to develop software efficiently while guaranteeing the quality, limiting the cost, and maintaining the development schedule. Therefore, the corporation and the division of the corporation that develop software are implementing various improvement methods, including process improvement. In this study we have analyzed data from a software development project and studied how to determine which software development project will fail because it takes more manpower that originally estimated. In addition, we have implemented project-management support tools by combining the project-monitoring and managing tools that are already in use. We have also developed a new model that uses the statistical method, by reviewing the tools that are already used to estimate manpower for new projects

Effort prediction model using similarity for embedded software development

In this paper, we propose an effort prediction model in which data including missing values is complemented by using the collaborative filtering [1, 2, 3] and the effort of projects is derived from a multiple regression analysis [4, 5] using the data. Because companies, recently, focus on methods to predict effort of projects, which prevent project failures such as exceeding deadline and cost, due to more complex embedded software, which brings the evolution of the performance and function enhancement [6, 7, 8]. Moreover, we conduct the evaluation experiment that compared the accuracy of our method with other two methods according to five criteria to confirm their accuracy. The results of the experiment shows that our method gives predictions the best in the five evaluation criteria.

Clustering and Analyzing Embedded Software Development Projects Data Using Self-Organizing Maps

In this paper, we cluster and analyze data from the past embedded software development projects using self-organizing maps (SOMs)[9] that are a type of artificial neural networks that rely on unsupervised learning. The purpose of the clustering and analysis is to improve the accuracy of predicting the number of errors. A SOMproduces a low-dimensional, discretized representation of the input space of training samples; these representations are called maps. SOMs are useful for visualizing low-dimensional views of high-dimensional data, a multidimensional scaling technique. The advantages of SOMs for statistical applications are as follows: (1) data visualization, (2) information processing on association and recollection, (3) summarizing large-scale data, and (4) creating nonlinear models. To verify our approach, we perform an evaluation experiment that compares SOM classification to product type classification using Welch’s t-test for Akaike’s Information Criterion (AIC). The results indicate that the SOM classification method is more contributive than product type classification in creating estimation models, because the mean AIC of SOM classification is statistically significantly lower.

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

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.

Error Prediction Methods for Embedded Software Development Using Hybrid Models of Self-Organizing Maps and Multiple Regression Analyses

In this study, we establish error prediction models at various stages of embedded software development using hybrid methods of self-organizing maps (SOMs) and multiple regression analyses (MRAs). SOMs are a type of artificial neural networks that relies on unsupervised learning. A SOM produces a low-dimensional, discretized representation of the input space of training samples; these representations are called maps. SOMs are useful for visualizing low-dimensional views of high-dimensional data as a multidimensional scaling technique. The advantages of SOMs for statistical applications are as follows: (1) enabling reasonable inferences to be made from incomplete information via association and recollection, (2) visualizing data, (3) summarizing large-scale data, and (4) creating nonlinear models. We focus on the first advantage to create error prediction models at various stages of embedded software development. In some cases, a model using only SOMs yields lower error prediction accuracy than a model using only MRAs. However, the opposite is true. Therefore, in order to improve prediction accuracy, we combine both models. To verify our approach, we perform an evaluation experiment that compares hybrid models to MRA models using Welch’s t test. The results of the comparison indicate that the hybrid models are more accurate than the MRA models for the mean of relative errors, because the mean errors of the hybrid models are statistically significantly lower.

Effort Estimation for Embedded Software Development Projects by Combining Machine Learning with Classification

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games–Howell test with one-way analysis of variance is performed to consider statistically significant evidence.