Abstract
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.
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References
Aoki, S.: In testing whether the means of two populations are different, http://aoki2.si.gunma-u.ac.jp/lecture/BF/index.html (in Japanese)
Boehm, B.: Software engineering. IEEE Trans. Software Eng. C-25(12), 1226–1241 (1976)
Hirayama, M.: Current state of embedded software. Journal of Information Processing Society of Japan (IPSJ) 45(7), 677–681 (2004) (in Japanese)
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)
Iwata, K., Nakashima, T., Anan, Y., Ishii, N.: Error estimation models integrating previous models and using artific ial neural networks for embedded software development projects. In: Proceedings of 20th IEEE International Conference on Tools with Artificial Intelligence, pp. 371–378 (2008)
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. Studies in Computational Intelligence, vol. 295, pp. 11–21. Springer, Heidelberg (2010)
Iwata, K., Nakashima, T., Anan, Y., Ishii, N.: Clustering and Analyzing Embedded Software Development Projects Data Using Self-Organizing Maps. In: Lee, R. (ed.) Software Engineering Research,Management and Applications 2011. SCI, vol. 377, pp. 47–59. Springer, Heidelberg (2012)
Iwata, K., Nakashima, T., Anan, Y., Ishii, N.: Effort prediction models using self-organizing maps for embedded software development projects. In: Proceedings of 23th IEEE International Conference on Tools with Artificial Intelligence, pp. 142–147 (2011)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer (2000)
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)
Ubayashi, N.: Modeling techniques for designing embedded software. Journal of Information Processing Society of Japan (IPSJ) 45(7), 682–692 (2004) (in Japanese)
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)
Nakashima, S.: Introduction to model-checking of embedded software. Journal of Information Processing Society of Japan (IPSJ) 45(7), 690–693 (2004) (in Japanese)
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)
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)
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)
Watanabe, H.: Product line technology for software development. Journal of Information Processing Society of Japan (IPSJ) 45(7), 694–698 (2004) (in Japanese)
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Iwata, K., Nakashima, T., Anan, Y., Ishii, N. (2013). Error Prediction Methods for Embedded Software Development Using Hybrid Models of Self-Organizing Maps and Multiple Regression Analyses. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2012. Studies in Computational Intelligence, vol 443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32172-6_15
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DOI: https://doi.org/10.1007/978-3-642-32172-6_15
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