240
Views
0
CrossRef citations to date
0
Altmetric
Articles

GVSEE: A Global Village Service Effort Estimator to Estimate Software Services Development Effort

, &

References

  • Ahmed, M. A., M. Omolade Saliu, and J. AlGhamdi. 2005. Adaptive fuzzy logic-based framework for software development effort prediction. Information and Software Technology 47 (1):31–48. doi:10.1016/j.infsof.2004.05.004.
  • Angelis, L., I. Stamelos, and M. Morisio. 2001. Building a software cost estimation model based on categorical data. In Proceedings of the seventh international software metrics symposium, METRICS 2001. IEEE.
  • Azzeh, M. 2012. A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation. Empirical Software Engineering 17 (1–2):90–127. doi:10.1007/s10664-011-9176-6.
  • Azzeh, M., D. Neagu, and P. I. Cowling. 2010. Fuzzy grey relational analysis for software effort estimation. Empirical Software Engineering 15 (1):60–90. doi:10.1007/s10664-009-9113-0.
  • Bardsiri, A. K., and S. M. Hashemi. 2013. Electronic services, the only way to realize the global village. International Journal of Mechatronics, Electrical and Computer Technology 3 (6):1039–41.
  • Bardsiri, A. K., and S. M. Hashemi. 2014. Software effort estimation: A survey of well-known approaches. International Journal of Computer Science Engineering 3 (1):46–50.
  • Bardsiri, V. K., D. N. A. Jawawi, S. Z. M. Hashim, and E. Khatibi. 2013. LMES: A localized multi-estimator model to estimate software development effort. Engineering Applications of Artificial Intelligence 26 (10):2624–40.
  • Bardsiri, V. K., D. N. A. Jawawi, A. K. Bardsiri, and E. Khatibi. 2013. A PSO-based model to increase the accuracy of software development effort estimation. Software Quality Journal 21 (3):501–26. doi:10.1007/s11219-012-9183-x.
  • Bardsiri, V. K., D. N. A. Jawawi, S. Z. M. Hashim, and E. Khatibi. 2014. A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons. Empirical Software Engineering 19 (4):857–84. doi:10.1007/s10664-013-9241-4.
  • Benala, T. R., R. Mall, S. Dehuri, and V. Prasanthi 2012. Software effort orediction using fuzzy clustering and functional link artificial neural networks. In Swarm, evolutionary, and memetic computing. Berlin Heidelberg: Springer.
  • Benala, T. R., R. Mall, P. Srikavya, and M. V. HariPriya 2014. Software effort estimation using data mining techniques. In ICT and critical infrastructure: Proceedings of the 48th annual convention of computer society of India-vol I. Switzerland: Springer.
  • Bettenburg, N., M. Nagappan, and A. E. Hassan. 2012. Think locally, act globally: Improving defect and effort prediction models. In 9th IEEE working conference on mining software repositories (MSR), 2012. Zurich: IEEE.
  • Breiman, L., J. Friedman, C. J. Stone, and R. A. Olshen. 1984. Classification and regression trees. New York: CRC press.
  • Dalkey, N., and O. Helmer. 1963. An experimental application of the delphi method to the use of experts. Management Science 9 (3):458–67. doi:10.1287/mnsc.9.3.458.
  • Dejaeger, K., W. Verbeke, D. Martens, and B. Baesens. 2012. Data mining techniques for software effort estimation: A comparative study. IEEE Transactions on Software Engineering 38 (2):375–97. doi:10.1109/TSE.2011.55.
  • Dolado, J. J. 2001. On the problem of the software cost function. Information and Software Technology 43 (1):61–72. doi:10.1016/S0950-5849(00)00137-3.
  • Foss, T., E. Stensrud, B. Kitchenham, and I. Myrtveit. 2003. A simulation study of the model evaluation criterion MMRE. IEEE Transactions on Software Engineering 29 (11):985–95. doi:10.1109/TSE.2003.1245300.
  • Hashemi, S. M., and M. Razzazi. 2011. Global village services as the future of electronic services. Saarbrücken, Germany: Lambert Academic Publishing.
  • Hashemi, S. M., M. Razzazi, and M. Teshnehlab. 2008. Streamlining the global village grid services. World Applied Sciences Journal 3 (5):824–32.
  • Hsu, C.-J., and C.-Y. Huang. 2011. Comparison of weighted grey relational analysis for software effort estimation. Software Quality Journal 19 (1):165–200. doi:10.1007/s11219-010-9110-y.
  • ISBSG (2011). International software benchmarking standard group. Data CD Release 11. www.isbsg.org.
  • Jones, C. 2007. Estimating software costs: Bringing realism to estimating. New York NY,: McGraw-Hill Companies.
  • Kadoda, G., M. Cartwright, L. Chen, and M. Shepperd. 2000. Experiences using case-based reasoning to predict software project effort. Paper presented at the Proceedings of the EASE Conference, Keele, UK, March 2000.
  • Khatibi, V., and D. N. A. Jawawi. 2011. Software cost estimation methods: A review Journal of emerging trends in computing and information sciences 2 (1):21–29.
  • Kocaguneli, E., and T. Menzies. 2013. Software effort models should be assessed via leave-one-out validation. Journal of Systems and Software 86 (7):1879–90. doi:10.1016/j.jss.2013.02.053.
  • Kocaguneli, E., T. Menzies, and J. W. Keung. 2012. On the value of ensemble effort estimation. IEEE Transactions on Software Engineering 38 (6):1403–16. doi:10.1109/TSE.2011.111.
  • Kocaguneli, E., T. Menzies, and J. W. Keung. 2013. Kernel methods for software effort estimation. Empirical Software Engineering 18 (1):1–24. doi:10.1007/s10664-011-9189-1.
  • Li, Y., M. Xie, and T. Goh. 2007. A study of genetic algorithm for project selection for analogy based software cost estimation. Paper presented at the IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, December 2–4.
  • Li, Y.-F., M. Xie, and T. N. Goh. 2009. A study of project selection and feature weighting for analogy based software cost estimation. Journal of Systems and Software 82 (2):241–52. doi:10.1016/j.jss.2008.06.001.
  • Limam, N., and R. Boutaba. 2010. Assessing software service quality and trustworthiness at selection time. IEEE Transactions on Software Engineering 36 (4):559–74. doi:10.1109/TSE.2010.2.
  • Lin, J.-C., and H.-Y. Tzeng. 2010. Applying particle swarm optimization to estimate software effort by multiple factors software project clustering. Paper presented at the IEEE International Computer Symposium (ICS), Taiwan, December 16–18.
  • MacDonell, S. G., and M. J. Shepperd. 2003. Combining techniques to optimize effort predictions in software project management. Journal of Systems and Software 66 (2):91–98. doi:10.1016/S0164-1212(02)00067-5.
  • Mair, C., M. Shepperd, and M. Jørgensen. 2005. An analysis of data sets used to train and validate cost prediction systems. ACM SIGSOFT software engineering notes, New York, NY: ACM.
  • Menzies, T., A. Butcher, D. Cok, A. Marcus, L. Layman, F. Shull, B. Turhan, and T. Zimmermann. 2013. Local versus global lessons for defect prediction and effort estimation. IEEE Transactions on Software Engineering 39 (6):822–34. doi:10.1109/TSE.2012.83.
  • Menzies, T., A. Butcher, A. Marcus, T. Zimmermann, and D. Cok. 2011. Local vs. global models for effort estimation and defect prediction. In Proceedings of the 2011 26th IEEE/ACM international conference on automated software engineering. IEEE Computer Society.
  • Moløkken-Østvold, K., and M. Jørgensen. 2004. Group processes in software effort estimation. Empirical Software Engineering 9 (4):315–34. doi:10.1023/B:EMSE.0000039882.39206.5a.
  • Nassif, A. B., L. F. Capretz, and D. Ho. 2012. Software effort estimation in the early stages of the software life cycle using a cascade correlation neural network model. Paper presented at the 13th ACIS IEEE International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel & Distributed Computing (SNPD), Kyoto, Japan, August 8–10.
  • Nassif, A. B., D. Ho, and L. F. Capretz. 2013. Towards an early software estimation using log-linear regression and a multilayer perceptron model. Journal of Systems and Software 86 (1):144–60. doi:10.1016/j.jss.2012.07.050.
  • Park, H., and S. Baek. 2008. An empirical validation of a neural network model for software effort estimation. Expert Systems with Applications 35 (3):929–37. doi:10.1016/j.eswa.2007.08.001.
  • Pickard, L., B. Kitchenham, and S. Linkman. 2001. Using simulated data sets to compare data analysis techniques used for software cost modelling. IEE Proceedings-Software 148 (6):165–74. doi:10.1049/ip-sen:20010621.
  • Pillai, S., and M. Jeyakumar. 2015. General regression neural network for software effort estimation of small programs using a single variable. In Power electronics and renewable energy systems. India: Springer.
  • Shepperd, M., and C. Schofield. 1997. Estimating software project effort using analogies. IEEE Transactions on Software Engineering 23 (11):736–43. doi:10.1109/32.637387.
  • Shepperd, M., and G. Kadoda. 2001. Comparing software prediction techniques using simulation. IEEE Transactions on Software Engineering 27 (11):1014–22. doi:10.1109/32.965341.
  • Shukla, R., M. Shukla, and T. Marwala. 2014. Neural network and statistical modeling of software development effort. In Proceedings of the second international conference on soft computing for problem solving (SocProS 2012). December 28–30, 2012, India: Springer.
  • Tansey, B., and E. Stroulia. 2007. Valuating software service development: integrating COCOMO II and real options theory. In Proceedings of the first international workshop on the economics of software and computation. May 20–26, 2007, Minneapolis, MN: IEEE Computer Society.
  • Trendowicz, A., and R. Jeffery. 2014. Principles of effort and cost estimation. In Software project effort estimation. Switzerland: Springer.
  • Wen, J., S. Li, Z. Lin, Y. Hu, and C. Huang. 2012. Systematic literature review of machine learning based software development effort estimation models. Information and Software Technology 54 (1):41–59. doi:10.1016/j.infsof.2011.09.002.
  • Wu, D., J. Li, and Y. Liang. 2013. Linear combination of multiple case-based reasoning with optimized weight for software effort estimation. The Journal of Supercomputing 64 (3):898–918. doi:10.1007/s11227-010-0525-9.
  • Zhang, W., Y. Yang, and Q. Wang. 2015. Using Bayesian regression and em algorithm with missing handling for software effort prediction. Information and Software Technology 58:58–70. doi:10.1016/j.infsof.2014.10.005.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.