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

  • Kazunori Iwata
  • Toyoshiro Nakashima
  • Yoshiyuki Anan
  • Naohiro Ishii
Part of the Studies in Computational Intelligence book series (SCI, volume 377)

Abstract

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.

Keywords

Self-organizing maps clustering embedded software development 

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References

  1. 1.
    Akaike, H.: Information theory and an extention of the maximum likelihood principle. In: Petrov, B.N., Csaki, F. (eds.) 2nd International Symposium on Information Theory, pp. 267–281 (1973)Google Scholar
  2. 2.
    Aoki, S.: In testing whether the means of two populations are different (in Japanese), http://aoki2.si.gunma-u.ac.jp/lecture/BF/index.html
  3. 3.
    Boehm, B.: Software engineering. IEEE Trans. Software Eng. C-25(12), 1226–1241 (1976)Google Scholar
  4. 4.
    Futagami, T.: Embedded software development tools update. Journal of Information Processing Society of Japan(IPSJ) 45(7), 704–712 (2004)Google Scholar
  5. 5.
    Hirayama, M.: Current state of embedded software. Journal of Information Processing Society of Japan(IPSJ) 45(7), 677–681 (2004) (in Japanese)Google Scholar
  6. 6.
    Iwata, K., Anan, Y., Nakashima, T., Ishii, N.: Using an artificial neural network for predicting embedded software development effort. In: Proceedings of 10th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing – SNPD 2009, pp. 275–280 (2009)Google Scholar
  7. 7.
    Iwata, K., Nakashima, T., Anan, Y., Ishii, N.: Error estimation models integrating previous models and using artificial neural networks for embedded software development projects. In: Proceedings of 20th IEEE International Conference on Tools with Artificial Intelligence, pp. 371–378 (2008)Google Scholar
  8. 8.
    Iwata, K., Nakashima, T., Anan, Y., Ishii, N.: 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.) SNPD 2010. SCI, vol. 295, pp. 11–21. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2000)Google Scholar
  10. 10.
    Komiyama, T.: Development of foundation for effective and efficient software process improvement. Journal of Information Processing Society of Japan(IPSJ) 44(4), 341–347 (2003) (in Japanese)Google Scholar
  11. 11.
    Ubayashi, N.: Modeling techniques for designing embedded software. Journal of Information Processing Society of Japan(IPSJ) 45(7), 682–692 (2004) (in japanese)Google Scholar
  12. 12.
    Nakamoto, Y., Takada, H., Tamaru, K.: Current state and trend in embedded systems. Journal of Information Processing Society of Japan(IPSJ) 38(10), 871–878 (1997) (in Japanese)Google Scholar
  13. 13.
    Nakashima, S.: Introduction to model-checking of embedded software. Journal of Information Processing Society of Japan(IPSJ) 45(7), 690–693 (2004) (in Japanese)Google Scholar
  14. 14.
    Ogasawara, H., Kojima, S.: Process improvement activities that put importance on stay power. Journal of Information Processing Society of Japan(IPSJ) 44(4), 334–340 (2003) (in Japanese)Google Scholar
  15. 15.
    Student: The probable error of a mean. Biometrika 6(1), 1–25 (1908)Google Scholar
  16. 16.
    Takagi, Y.: A case study of the success factor in large-scale software system development project. Journal of Information Processing Society of Japan(IPSJ) 44(4), 348–356 (2003) (in Japanese)Google Scholar
  17. 17.
    Tamaru, K.: Trends in software development platform for embedded systems. Journal of Information Processing Society of Japan(IPSJ) 45(7), 699–703 (2004) (in Japanese)Google Scholar
  18. 18.
    Watanabe, H.: Product line technology for software development. Journal of Information Processing Society of Japan(IPSJ) 45(7), 694–698 (2004) (in Japanese)Google Scholar
  19. 19.
    Welch, B.L.: The generalization of student’s problem when several different population variances are involved. Biometrika 34(28) (1947)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kazunori Iwata
    • 1
  • Toyoshiro Nakashima
    • 2
  • Yoshiyuki Anan
    • 3
  • Naohiro Ishii
    • 4
  1. 1.Department of Business AdministrationAichi UniversityMiyoshiJapan
  2. 2.Department of Culture-Information StudiesSugiyama Jogakuen UniversityNagoyaJapan
  3. 3.Base DivisionOmron Software Co., Ltd.Shimogyo-kuJapan
  4. 4.Department of Information ScienceAichi Institute of TechnologyToyotaJapan

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