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Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010 pp 11–21Cite as

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

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 295))

Abstract

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.

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Author information

Authors and Affiliations

  1. Dept. of Business Administration, Aichi University, 370 Shimizu, Kurozasa-cho, Miyosh, Aichi, 470-0296, Japan

    Kazunori Iwata

  2. Dept. of Culture-Information Studies, Sugiyama Jogakuen University, 17-3, Moto-machi, Hoshigaoka, Chikusa-ku, Nagoya, Aichi, 464-8662, Japan

    Toyoshiro Nakashima

  3. Omron Software Co., Ltd., Shiokoji Horikawa, Shimogyo-ku, Kyoto, 600-8234, Japan

    Yoshiyuki Anan

  4. Dept. of Information Science, Aichi Institute of Technology, 1247 Yachigusa, Yakusa-cho, Toyota, Aichi, 470-0392, Japan

    Naohiro Ishii

Authors
  1. Kazunori Iwata
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  2. Toyoshiro Nakashima
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  3. Yoshiyuki Anan
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  4. Naohiro Ishii
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Editor information

Editors and Affiliations

  1. Software Engineering & Information Technology Institute, Computer Science Department, Central Michigan University, 48859, Mt. Pleasant, MI, U.S.A.

    Roger Lee

  2. The University of Greenwich, London, UK

    Jixin Ma , Liz Bacon  & Miltos Petridis ,  & 

  3. Hainan University, Hainan, China

    Wencai Du

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© 2010 Springer-Verlag Berlin Heidelberg

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Iwata, K., Nakashima, T., Anan, Y., Ishii, N. (2010). Improving Accuracy of an Artificial Neural Network Model to Predict Effort and Errors in Embedded Software Development Projects. In: Lee, R., Ma, J., Bacon, L., Du, W., Petridis, M. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010. Studies in Computational Intelligence, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13265-0_2

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  • DOI: https://doi.org/10.1007/978-3-642-13265-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13264-3

  • Online ISBN: 978-3-642-13265-0

  • eBook Packages: EngineeringEngineering (R0)

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