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Machine Learning Classification to Effort Estimation for Embedded Software Development Projects
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Author(s): Kazunori Iwata (Department of Business Administration, Aichi University, Nagoya, Japan), Toyoshiro Nakashima (Department of Culture-Information Studies, Sugiyama Jogakuen University, Nagoya, Japan), Yoshiyuki Anan (Process Innovation H.Q, Omron Software Co., Ltd., Kyoto, Japan)and Naohiro Ishii (Department of Information Science, Aichi Institute of Technology, Nagoya, Japan)
Copyright: 2022
Pages: 14
Source title:
Research Anthology on Agile Software, Software Development, and Testing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-3702-5.ch078
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Abstract
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.
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