A Novel Technique of Optimization for the COCOMO II Model Parameters using Teaching-Learning-Based Optimization Algorithm

• Autorzy: Khuat T. T. , Le M. H.
• Języki publikacji: EN

Abstrakty

EN

Software cost estimation is a critical activity in the development life cycle for controlling risks and planning project schedules. Accurate estimation of the cost before the start-up of a project is essential for both the developers and the customers. Therefore, many models were proposed to address this issue, in which COCOMO II has been being widely employed in actual software projects. Good estimation models, such as COCOMO II, can avoid insufficient resources being allocated to a project. However, parameters for estimation formula in this model have not been optimized yet, and so the estimated results are not close to the actual results. In this paper, a novel technique to optimize the coefficients for COCOMO II model by using teaching-learning-based optimization (TLBO) algorithm is proposed. The performance of the model after optimizing parameters was tested on NASA software project dataset. The obtained results indicated that the improvement of parameters provided a better estimation capabilities compared to the original COCOMO II model.

Słowa kluczowe

EN

COCOMO II; cost estimation; NASA software; optimization; teaching-learning-based optimization algorithm

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2016
• Tom: nr 1
• Strony: 84--89
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Khuat T. T.

• The University of Danang, University of Science and Technology 54 Nguyen Luong Bang, Lien Chieu Danang, Vietnam

autor

Le M. H.

• The University of Danang, University of Science and Technology 54 Nguyen Luong Bang, Lien Chieu Danang, Vietnam

Bibliografia

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• [2] B. Clark, S. Devnani-Chulani, and B. Boehm, “Calibrating the COCOMO II post-architecture model”, in Proc. 20th Int. Conf. Softw. Engin. ICSE, Kyoto, Japan, 1998, pp. 477–480.
• [3] T. Menzies, The Tera-PROMISE Repository for COCOMO 93, 2015 [Online]. Available: http://openscience.us/repo/effort/cocomo/ nasa93.html
• [4] B. Boehm, B. Clark, E. Horowitz, C. Westland, R. Madachy, and R. Selby, “Cost Models for Future Software Life Cycle Processes: COCOMO 2.0”, Annals of Softw. Engin., vol. 1, no. 1, pp. 57–94, 1995.
• [5] C. Abts, B. Clark, S. Devnani-Chulani, E. Horowitz, R. Madachy, D. Reifer, R. Selby, and B. Steece, “COCOMO II Model Definition Manual”, Tech. Rep., Center for Software Engineering, University of Southern California, Los Angeles, CA, USA, 1998.
• [6] J. Kaur and R. Sindhu, “Parameter estimation of COCOMO II using tabu search”, Int. J. Comp. Sci. Inform. Technol., vol. 5, no. 3, pp. 4463–4465, 2014.
• [7] Z. Chen, T. Menzies, D. Port, and B. Boehm, “Feature Subset Selection Can Improve Software Cost Estimation Accuracy”, in Proc. Int. Worksh. Predic. Models in Softw. Engin. PROMISE 2005, St. Louis, MO, USA, 2005, pp. 1–6.
• [8] R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-LearningBased optimization: A novel method for constrained mechanical design optimization problems”, Computer-Aided Design, vol. 43, pp. 303–315, 2011.
• [9] B. Boehm, C. Abts, and S. Chulani, “Software development cost estimation approaches – A survey”, Annals of Softw. Engin., vol. 10, no. 1–4, pp. 177-205, 2000.
• [10] C. Jones, “A short history of software estimation tools”, Tech. Rep., VP and CTO, Namcook Analytics LLC, Narragansett, RI, USA, 2013.
• [11] T. Menzies, B. Boehm, Y. Yang, J. Hihn, and N. Lekkalapudi, “Just how good is COCOMO and parametric estimation”, in Proc. 29th Int. Forum on COCOMO and Syst. Softw. Cost Model., Los Angeles, CA, USA, 2014 [Online]. Available: http://csse.usc.edu/ new/events/cocomo-2014/program.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.