COSMIC FP method in software development estimation using artificial neural networks based on orthogonal arrays

References

• Angara, J., Prasad, S., & Sridevi, G. (2018). Towards Benchmarking user stories estimation with COSMIC function points-A case example of participant observation. International Journal of Electrical & Computer Engineering, 8(5), 3076–3083. https://doi.org/10.11591/ijece.v8i5.pp3076-3083
   Google Scholar
• Bagriyanik, S., & Karahoca, A. (2018). Using data mining to identify COSMIC function point measurement competence. International Journal of Electrical and Computer Engineering, 8(6), 5253–5259. https://doi.org/10.11591/ijece.v8i6.pp5253-5259
   Google Scholar
• Borandag, E., Ozcift, A., Kilinc, D., & Yucalar, F. (2019). Majority vote Feature Selection algorithm in software fault prediction. Computer Science and Information Systems, 16(2), 515–539. https://doi.org/10.2298/CSIS180312039B
   Web of Science ®Google Scholar
• CHAOS Report. (2013). The Standish Group.
   Google Scholar
• Chhabra, S., & Singh, H. (2020). Optimizing design of Fuzzy model for software cost estimation using particle swarm optimization algorithm. International Journal of Computational Intelligence and Applications, 19(1), 2050005. https://doi.org/10.1142/S1469026820500054
   Web of Science ®Google Scholar
• Cuadrado-Gallego, J. J., Buglione, L., Rejas-Muslera, R. J., & Machado-Piriz, F. (2008, September 3–5). IFPUG-COSMIC statistical conversion. 34th Euromicro Conference Software Engineering and Advanced Applications, Parma, Italy (pp. 427–432). IEEE. https://doi.org/10.1109/SEAA.2008.75
   Google Scholar
• De Vito, G., Ferrucci, F., & Gravino, C. (2020). Design and automation of a COSMIC measurement procedure based on UML models. Software and Systems Modeling, 19(1), 171–198. https://doi.org/10.1007/s10270-019-00731-2
   Web of Science ®Google Scholar
• Di Martino, S., Ferrucci, F., Gravino, C., & Sarro, F. (2016). Web effort estimation: Function point analysis vs. COSMIC. Information and Software Technology, 72, 90–109. https://doi.org/10.1016/j.infsof.2015.12.001
   Web of Science ®Google Scholar
• Di Martino, S., Ferrucci, F., Gravino, C., & Sarro, F. (2020). Assessing the effectiveness of approximate functional sizing approaches for effort estimation. Information and Software Technology, 123, 106308. https://doi.org/10.1016/j.infsof.2020.106308
   Web of Science ®Google Scholar
• Fehlmann, T., & Santillo, L. (2010, November). From story points to cosmic function points in agile software development – A six sigma perspective. Metrikon-Software Metrik Kongress (p. 24).
   Google Scholar
• Fu, T., Tang, X., Cai, Z., Zuo, Y., Tang, Y., & Zhao, X. (2020). Correlation research of phase angle variation and coating performance by means of Pearson’s correlation coefficient. Progress in Organic Coatings, 139, 105–459. https://doi.org/10.1016/j.porgcoat.2019.105459
   Web of Science ®Google Scholar
• Goyal, S., & Parashar, A. (2018). Machine learning application to improve COCOMO model using neural networks. International Journal of Information Technology and Computer Science (IJITCS), 10(3), 35–51. https://doi.org/10.5815/ijitcs.2018.03.05
   Google Scholar
• Hussain, I., Kosseim, L., & Ormandjieva, O. (2013). Approximation of COSMIC functional size to support early effort estimation in agile. Data & Knowledge Engineering, 85, 2–14. https://doi.org/10.1016/j.datak.2012.06.005
   Web of Science ®Google Scholar
• International Software Benchmarking Standards Group (ISBSG). (2015). ISBSG repository from https://www.isbsg.org/
   Google Scholar
• Janković, M., Žitnik, S., & Bajec, M. (2019). Reconstructing De facto software development methods. Computer Science and Information Systems, 16(1), 75–104. https://doi.org/10.2298/CSIS180226038J
   Web of Science ®Google Scholar
• Jiang, Y., Liang, W., Tang, J., Zhou, H., Li, K. C., & Gaudiot, J. L. (2021). A novel data representation framework based on nonnegative manifold regularisation. Connection Science, 33(2), 136–152. https://doi.org/10.1080/09540091.2020.1772722
   Web of Science ®Google Scholar
• Jiao, J., Wang, L., Li, Y., Han, D., Yao, M., Li, K. C., & Jiang, H. (2021). CASH: Correlation-aware scheduling to mitigate soft error impact on heterogeneous multicores. Connection Science, 33(2), 113–135. https://doi.org/10.1080/09540091.2020.1758924
   Web of Science ®Google Scholar
• Kataev, M., Bulysheva, L., Xu, L., Ekhlakov, Y., Permyakova, N., & Jovanovic, V. (2020). Fuzzy model estimation of the risk factors impact on the target of promotion of the software product. Enterprise Information Systems, 14(6), 797–811. https://doi.org/10.1080/17517575.2020.1713407
   Web of Science ®Google Scholar
• Kaur, A., & Kaur, K. (2019). A COSMIC function points based test effort estimation model for mobile applications. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2019.03.001
   Google Scholar
• Khaw, J. F. C., Lim, B. S., & Lim, L. E. N. (1995). Optimal design of neural networks using the Taguchi method. Neurocomputing, 7 (30), 225–245. https://doi.org/10.1016/0925-2312(94)00013-I
   Google Scholar
• Kheyrinataj, F., & Nazemi, A. (2020). Fractional power series neural network for solving delay fractional optimal control problems. Connection Science, 32(1), 53–80. https://doi.org/10.1080/09540091.2019.1605498
   Web of Science ®Google Scholar
• Lavazza, L., & Liu, G. (2019, November 24–28). An empirical evaluation of the accuracy of NESMA function points estimates. The Fourteenth International Conference on Software Engineering Advances (ICSEA), Valencia, Spain.
   Google Scholar
• Meng, J., Ricco, M., Luo, G., Swierczynski, M., Stroe, D. I., Stroe, A. I., & Teodorescu, R. (2017). An overview and comparison of online implementable SOC estimation methods for lithium-ion battery. IEEE Transactions on Industry Applications, 54(2), 1583–1591. https://doi.org/10.1109/TIA.2017.2775179
   Web of Science ®Google Scholar
• Moosavi, S. H. S., & Bardsiri, V. K. (2017). Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation. Engineering Applications of Artificial Intelligence, 60, 1–15. https://doi.org/10.1016/j.engappai.2017.01.006
   Web of Science ®Google Scholar
• Ochodek, M., Kopczyńska, S., & Staron, M. (2020). Deep learning model for end-to-end approximation of COSMIC functional size based on use-case names. Information and Software Technology, 123, 106310. https://doi.org/10.1016/j.infsof.2020.106310
   Web of Science ®Google Scholar
• Popović, J., & Bojić, D. (2012). A comparative evaluation of effort estimation methods in the software life cycle. Computer Science and Information Systems, 9(1), 455–484. https://doi.org/10.2298/CSIS110316068P
   Web of Science ®Google Scholar
• Potha, N., Kouliaridis, V., & Kambourakis, G. (2021). An extrinsic random-based ensemble approach for android malware detection. Connection Science, 33(4), 1077–1093. https://doi.org/10.1080/09540091.2020.1853056
   Web of Science ®Google Scholar
• Qiu, L., Sai, S., & Tian, X. (2021). TsFSIM: A three-step fast selection algorithm for influence maximisation in social network. Connection Science, 33(4), 854–869. https://doi.org/10.1080/09540091.2021.1904206
   Web of Science ®Google Scholar
• Quesada-López, C., Madrigal-Sánchez, D., & Jenkins, M. (2020, February). An empirical analysis of IFPUG FPA and COSMIC FP measurement methods. International Conference on Information Technology & Systems (pp. 265–274). Springer.
   Google Scholar
• Rankovic, D., Rankovic, N., Ivanovic, M., & Lazic, L. (2021). Convergence rate of artificial neural networks for estimation in software development projects. Information and Software Technology, 138, 106627. https://doi.org/10.1016/j.infsof.2021.106627
   Web of Science ®Google Scholar
• Rankovic, N., Rankovic, D., Ivanovic, M., & Lazic, L. (2021). A new approach to software effort estimation using different artificial neural network architectures and Taguchi Orthogonal Arrays. IEEE Access, 9, 26926–26936. https://doi.org/10.1109/ACCESS.2021.3057807
   Web of Science ®Google Scholar
• ReliaSoft Publishing. (2012, January). Taguchi Orthogonal Array Designs from https://www.weibull.com/hotwire/issue131/hottopics131.htm
   Google Scholar
• Sabaghian, A., Balochian, S., & Yaghoobi, M. (2020). Synchronisation of 6D hyper-chaotic system with unknown parameters in the presence of disturbance and parametric uncertainty with unknown bounds. Connection Science, 32(4), 362–383. https://doi.org/10.1080/09540091.2020.1723491
   Web of Science ®Google Scholar
• Sakhrawi, Z., Sellami, A., & Bouassida, N. (2020). Investigating the impact of functional size measurement on predicting software enhancement effort using correlation-based feature selection algorithm and SVR method. In S. Ben Sassi, S. Ducasse, & H. Mili (Eds.), Reuse in emerging software engineering practices (19th International Conference on Software and Systems Reuse, ICSR 2020, Hammamet, Tunisia, Lecture notes in Computer Science, Vol. 12541, pp. 229-244). Springer. https://doi.org/10.1007/978-3-030-64694-3_14
   Google Scholar
• Salmanoglu, M., Hacaloglu, T., & Demirors, O. (2017). Effort estimation for agile software development: comparative case studies using COSMIC functional size measurement and story points. Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement (IWSM Mensura ‘17). Association for Computing Machinery, New York, NY (pp. 41–49). https://doi.org/10.1145/3143434.3143450
   Google Scholar
• Stoica, A., & Blosiu, J. (1997). Neural learning using orthogonal arrays. Advances in Intelligent Systems, 41, 418–423. ISBN Print: 978-90-5199-355-4.
   Google Scholar
• Subasri, R., Meenakumari, R., Panchal, H., Suresh, M., Priya, V., Ashokkumar, R., & Sadasivuni, K. K. (2020). Comparison of BPN, RBFN and wavelet neural network in induction motor modelling for speed estimation. International Journal of Ambient Energy, 17, 1–6. https://doi.org/10.1080/01430750.2020.1817779
   Google Scholar
• Symons, C. R. (1988). Function point analysis: Difficulties and improvements. IEEE Transactions on Software Engineering, 14(1), 2–11. https://doi.org/10.1109/32.4618
   Web of Science ®Google Scholar
• The University of York, Department of Mathematics. (2004, May). Orthogonal Arrays (Taguchi Designs) from https://www.york.ac.uk/depts/maths/tables/orthogonal.htm
   Google Scholar
• Ugalde, F., Quesada-López, C., Martínez, A., & Jenkins, M. (2020, November). A comparative study on measuring software functional size to support effort estimation in agile. CIBSE, Brazil, American Conference on Software Engineering.
   Google Scholar
• Xia, W., Capretz, L. F., Ho, D., & Ahmed, F. (2008). A new calibration for function point complexity weights. Information and Software Technology, 50(7–8), 670–683. https://doi.org/10.1016/j.infsof.2007.07.004
   Web of Science ®Google Scholar
• Xia, W., Ho, D., & Capretz, L. F. (2015). A neuro-fuzzy model for function point calibration. WSEAS Transactions on Information Science and Applications, 5(1), 22–30.
   Google Scholar
• Younas, M., Jawawi, D. N., Ghani, I., & Shah, M. A. (2020). Extraction of non-functional requirement using semantic similarity distance. Neural Computing and Applications, 32(11), 7383–7397. https://doi.org/10.1007/s00521-019-04226-5
   Web of Science ®Google Scholar
• Zhang, L., Lu, D., & Wang, X. (2020). Measuring and testing interdependence among random vectors based on Spearman’s $$ρ $$ ρ and Kendall’s $$τ $$ τ. Computational Statistics, 35(4), 1685–1713. https://doi.org/10.1007/s00180-020-00973-5
   Web of Science ®Google Scholar
• https://cosmic-sizing.org/publications/early-software-sizing-with-cosmic-practitioners-guide/
   Google Scholar