Abstract
During software development, early identification of critical components is of much practical significance since it facilitates allocation of adequate resources to these components in a timely fashion and thus enhance the quality of the delivered system. The purpose of this paper is to develop a classification model for evaluating the criticality of software components based on their software characteristics. In particular, we employ the radial basis function machine learning approach for model development where our new, innovative algebraic algorithm is used to determine the model parameters. For experiments, we used the USA-NASA metrics database that contains information about measurable features of software systems at the component level. Using our principled modeling methodology, we obtained parsimonious classification models with impressive performance that involve only design metrics available at earlier stage of software development. Further, the classification modeling approach was non-iterative thus avoiding the usual trial-and-error model development process.
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References
Shin, M., Goel, A.L.: A. Empirical Data Modeling in Software Engineering using Radial Basis Functions. IEEE Transactions on Software Engineering (28), 567–576 (2002)
Khoshgoftaar, T., Seliya, N.: Comparative Assessment of Empirical Software Quality Classification Techniques: An Empirical Case study. Software Engineering 9, 229–257 (2004)
Khoshgoftaar, T., Yuan, X., Aleen, E.B., Jones, W.D., Hudepohl, J.P.: Uncertain Classification of Fault-Prone Software Modules. Empirical Software Engineering 7, 297–318 (2002)
Lanubile, F., Lonigro, A., Vissagio, G.: Comparing Models for identifying Fault-Prone Software Components. In: 7th International Conference on Software Engineering and Knowledge Engineering, Rockville, Maryland, June 1995, pp. 312–319 (1995)
Pedrycz, W.: Computational Intelligence as an Emerging Paradigm of Software Engineering. In: Fourteenth International Conference on Software Engineering and Knowledge Engineering, Ischia, Itlay, July 2002, pp. 7–14 (2002)
Pighin, M., And Zamolo, R.: A Predictive Metric based on Discriminant Statistical Analysis. In: International Conference on Software Engineering, Boston, MA, pp. 262–270 (1997)
Zhang, D., And Tsai, J.J.P.: Machine Learning and Software Engineering. Software Quality journal 11, 87–119 (2003)
Shull, F., Mendonce, M.G., Basili, V., Carver, J., Maldonado, J.C., Fabbri, S., Travassos, G.H., Ferreira, M.C.: Knowledge-Sharing Issues in Experimental Software Engineering. Empirical Software Engineering 9, 111–137 (2004)
Goel, A.L., Shin, M.: Tutorial on Software Models and Metrics. In: International Conference on Software Engineering, Boston, MA (1997)
Han, J., Kamber, M.: Data Mining. Morgan Kauffman, San Francisco (2001)
Shin, M., Goel, A.L.: Design and Evaluation of RBF Models based on RC Criterion, Technical Report (2003), Syracuse University
Khoshgoftaar, T., et al.: Uncertain Classification of Fault Prone Software Modules. Empirical Software Engineering 7, 297–318 (2002)
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© 2005 Springer-Verlag Berlin Heidelberg
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Shin, M., Goel, A.L. (2005). Modeling Software Component Criticality Using a Machine Learning Approach. In: Kim, T.G. (eds) Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science(), vol 3397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30583-5_47
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DOI: https://doi.org/10.1007/978-3-540-30583-5_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24476-9
Online ISBN: 978-3-540-30583-5
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