INTEGRATION OF FUZZY LOGIC METHOD AND COCOMO II ALGORITHM TO IMPROVE PREDICTION TIMELINESS AND SOFTWARE DEVELOPMENT COST
Keywords:
COCOMO II, Fuzzy Logic, Software Cost Estimation, Gaussian Membership Function (2-D GMF).
Abstract
This study discusses improving the prediction of timeliness and cost of software development using the Constructive Cost Model II (COCOMO II) method and the application of Fuzzy Logic. And aims to obtain accurate time and cost prediction estimates on software development projects to obtain maximum cost results for a software development project. This study utilizes an adaptive fuzzy logic model to improve the timeliness of software development and cost estimates. Using the advantages of fuzzy set logic and producing accurate software attributes to increase the prediction of the time and price of software development. The fuzzy model uses the Two-D Gaussian Membership Function (2-D GMF) to make the software attributes more detailed in terms of the range of values. In COCOMO I, NASA98 data set; and four data projects from software companies in Indonesia were used to evaluate the proposed Fuzzy Logic COCOMO II, commonly known as FL-COCOMO II. Using the Mean of Magnitude of Relative Error (MMRE) and the Pred evaluation technique, the results showed that FL-COCOMO II produced less MMRE than COCOMO I, and the Pred value (25%) in Fuzzy-COCOMO II was higher than COCOMO I. In addition, FL-COCOMO II showed an 8.03% increase in prediction accuracy using MMRE compared to the original COCOMO. Using the advantages of Fuzzy Logic, such as accurate predictions, adaptation, and understanding can improve the accuracy of the timeliness and cost estimates of the software.
References
Baiquni, M., Sarno, R., Sarwosri, & Sholiq. (2017). Improving the accuracy of COCOMO II using fuzzy logic and local calibration method. 2017 3rd International Conference on Science in Information Technology (ICSITech), 284–289. https://doi.org/10.1109/ICSITech.2017.8257126
Bedi, R. P. S., & Singh, A. (2017). Software Cost Estimation using Fuzzy Logic Technique. Indian Journal of Science and Technology, 10(3). https://doi.org/10.17485/ijst/2017/v10i3/109997
Christina, M. A., & Banumathy, C. (2019). Software cost estimation using neuro fuzzy logic Framework. International Journal of Research in Engineering, Science and Management, 2(1), 219–224.
Huang, X., Capretz, L., Ren, J., & Ho, D. (2003). A Neuro-Fuzzy Model for Software Cost Estimation. https://doi.org/10.1109/QSIC.2003.1319094
Huang, X., Ho, D., Ren, J., & Capretz, L. (2007). Improving the COCOMO model using a neuro-fuzzy approach. Applied Soft Computing, 7, 29–40. https://doi.org/10.1016/j.asoc.2005.06.007
Indra, M., & Aqlani, Z. (2018). Comparative Analyisis of Software Cost Estimation Project using Algorithmic Method. Engineering Software Requirements, 1(1), 17–27.
Iqbal, N., & Sang, J. (2021). Fuzzy Logic Testing Approach for Measuring Software Completeness. Symmetry, 13, 604. https://doi.org/10.3390/sym13040604
Kaur, I., Narula, G. S., Wason, R., Jain, V., & Baliyan, A. (2018). Neuro fuzzy—COCOMO II model for software cost estimation. International Journal of Information Technology, 10(2), 181–187. https://doi.org/10.1007/s41870-018-0083-6
Langsari, K, & Sarno, R. (2017a). Optimizing COCOMO II parameters using particle swarm method. 2017 3rd International Conference on Science in Information Technology (ICSITech), 29–34. https://doi.org/10.1109/ICSITech.2017.8257081
Langsari, K, & Sarno, R. (2017b). Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 1–6. https://doi.org/10.1109/EECSI.2017.8239157
Langsari, Kholed, Sarno, R., & Sholiq. (2018). Optimizing time and effort parameters of COCOMO II using fuzzy Multi-objective Particle Swarm Optimization. Telkomnika (Telecommunication Computing Electronics and Control), 16(5), 2199–2207. https://doi.org/10.12928/TELKOMNIKA.v16i5.9698
Malik, A., Pandey, V., & Kaushik, A. (2013). An Analysis of Fuzzy Approaches for COCOMO II. International Journal of Intelligent Systems and Applications, 5(5), 68–75. https://doi.org/10.5815/ijisa.2013.05.08
Molokken, K., & Jorgensen, M. (2003). A review of software surveys on software effort estimation. 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings., 223–230. https://doi.org/10.1109/ISESE.2003.1237981
Parwita, I. M. M., Sarno, R., & Puspaningrum, A. (2017). Optimization of COCOMO II coefficients using Cuckoo optimization algorithm to improve the accuracy of effort estimation. 2017 11th International Conference on Information & Communication Technology and System (ICTS), 99–104. https://doi.org/10.1109/ICTS.2017.8265653
Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A. (2018). An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software. https://doi.org/10.1016/j.jss.2017.11.066
Putri, R. R., Sarno, R., Siahaan, D., Ahmadiyah, A., & Rochimah, S. (2017). Accuracy Improvement of the Estimations Effort in Constructive Cost Model II Based on Logic Model of Fuzzy. Advanced Science Letters, 23, 2478–2480. https://doi.org/10.1166/asl.2017.8767
Raza, K. (2019). Fuzzy logic based approaches for gene regulatory network inference. Artificial Intelligence in Medicine, 97, 189–203. https://doi.org/10.1016/j.artmed.2018.12.004
Sarno, R., Sidabutar, J., & Sarwosri. (2015). Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model. 2015 International Conference on Information & Communication Technology and Systems (ICTS), 197–202. https://doi.org/10.1109/ICTS.2015.7379898
Singal, P., Kumari, A. C., & Sharma, P. (2020). Estimation of Software Development Effort: A Differential Evolution Approach. Procedia Computer Science, 167(2019), 2643–2652. https://doi.org/10.1016/j.procs.2020.03.343
Sinha, R. R., & Gora, R. K. (2021). Software effort estimation using machine learning techniques. In Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-981-15-5421-6_8
Subandri, M. A., & Sarno, R. (2017). Cyclomatic Complexity for Determining Product Complexity Level in COCOMO II. Procedia Computer Science, 124, 478–486. https://doi.org/10.1016/j.procs.2017.12.180
Suherman, I. C., Sarno, R., & Sholiq. (2020). Implementation of Random Forest Regression for COCOMO II Effort Estimation. 2020 International Seminar on Application for Technology of Information and Communication (ISemantic), 476–481. https://doi.org/10.1109/iSemantic50169.2020.9234269
Tahir, F., & Adil, M. (2018). An Empirical Analysis of Cost Estimation Models on Undergraduate Projects Using COCOMO II. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 1–5. https://doi.org/10.1109/ICSCEE.2018.8538361
Yadav, R. (2017). OPTIMIZED MODEL FOR SOFTWARE EFFORT ESTIMATION USING COCOMO-2 METRICS WITH FUZZY LOGIC. International Journal of Advanced Research in Computer Science, 8, 121–125. https://doi.org/10.26483/ijarcs.v8i7.4113
Zhang, L. (2019). The Research on General Case-Based Reasoning Method Based on TF-IDF. 2019 2nd International Conference on Safety Produce Informatization (IICSPI), 670–673. https://doi.org/10.1109/IICSPI48186.2019.9095927
Bedi, R. P. S., & Singh, A. (2017). Software Cost Estimation using Fuzzy Logic Technique. Indian Journal of Science and Technology, 10(3). https://doi.org/10.17485/ijst/2017/v10i3/109997
Christina, M. A., & Banumathy, C. (2019). Software cost estimation using neuro fuzzy logic Framework. International Journal of Research in Engineering, Science and Management, 2(1), 219–224.
Huang, X., Capretz, L., Ren, J., & Ho, D. (2003). A Neuro-Fuzzy Model for Software Cost Estimation. https://doi.org/10.1109/QSIC.2003.1319094
Huang, X., Ho, D., Ren, J., & Capretz, L. (2007). Improving the COCOMO model using a neuro-fuzzy approach. Applied Soft Computing, 7, 29–40. https://doi.org/10.1016/j.asoc.2005.06.007
Indra, M., & Aqlani, Z. (2018). Comparative Analyisis of Software Cost Estimation Project using Algorithmic Method. Engineering Software Requirements, 1(1), 17–27.
Iqbal, N., & Sang, J. (2021). Fuzzy Logic Testing Approach for Measuring Software Completeness. Symmetry, 13, 604. https://doi.org/10.3390/sym13040604
Kaur, I., Narula, G. S., Wason, R., Jain, V., & Baliyan, A. (2018). Neuro fuzzy—COCOMO II model for software cost estimation. International Journal of Information Technology, 10(2), 181–187. https://doi.org/10.1007/s41870-018-0083-6
Langsari, K, & Sarno, R. (2017a). Optimizing COCOMO II parameters using particle swarm method. 2017 3rd International Conference on Science in Information Technology (ICSITech), 29–34. https://doi.org/10.1109/ICSITech.2017.8257081
Langsari, K, & Sarno, R. (2017b). Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 1–6. https://doi.org/10.1109/EECSI.2017.8239157
Langsari, Kholed, Sarno, R., & Sholiq. (2018). Optimizing time and effort parameters of COCOMO II using fuzzy Multi-objective Particle Swarm Optimization. Telkomnika (Telecommunication Computing Electronics and Control), 16(5), 2199–2207. https://doi.org/10.12928/TELKOMNIKA.v16i5.9698
Malik, A., Pandey, V., & Kaushik, A. (2013). An Analysis of Fuzzy Approaches for COCOMO II. International Journal of Intelligent Systems and Applications, 5(5), 68–75. https://doi.org/10.5815/ijisa.2013.05.08
Molokken, K., & Jorgensen, M. (2003). A review of software surveys on software effort estimation. 2003 International Symposium on Empirical Software Engineering, 2003. ISESE 2003. Proceedings., 223–230. https://doi.org/10.1109/ISESE.2003.1237981
Parwita, I. M. M., Sarno, R., & Puspaningrum, A. (2017). Optimization of COCOMO II coefficients using Cuckoo optimization algorithm to improve the accuracy of effort estimation. 2017 11th International Conference on Information & Communication Technology and System (ICTS), 99–104. https://doi.org/10.1109/ICTS.2017.8265653
Pospieszny, P., Czarnacka-Chrobot, B., & Kobylinski, A. (2018). An effective approach for software project effort and duration estimation with machine learning algorithms. Journal of Systems and Software. https://doi.org/10.1016/j.jss.2017.11.066
Putri, R. R., Sarno, R., Siahaan, D., Ahmadiyah, A., & Rochimah, S. (2017). Accuracy Improvement of the Estimations Effort in Constructive Cost Model II Based on Logic Model of Fuzzy. Advanced Science Letters, 23, 2478–2480. https://doi.org/10.1166/asl.2017.8767
Raza, K. (2019). Fuzzy logic based approaches for gene regulatory network inference. Artificial Intelligence in Medicine, 97, 189–203. https://doi.org/10.1016/j.artmed.2018.12.004
Sarno, R., Sidabutar, J., & Sarwosri. (2015). Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model. 2015 International Conference on Information & Communication Technology and Systems (ICTS), 197–202. https://doi.org/10.1109/ICTS.2015.7379898
Singal, P., Kumari, A. C., & Sharma, P. (2020). Estimation of Software Development Effort: A Differential Evolution Approach. Procedia Computer Science, 167(2019), 2643–2652. https://doi.org/10.1016/j.procs.2020.03.343
Sinha, R. R., & Gora, R. K. (2021). Software effort estimation using machine learning techniques. In Lecture Notes in Networks and Systems. https://doi.org/10.1007/978-981-15-5421-6_8
Subandri, M. A., & Sarno, R. (2017). Cyclomatic Complexity for Determining Product Complexity Level in COCOMO II. Procedia Computer Science, 124, 478–486. https://doi.org/10.1016/j.procs.2017.12.180
Suherman, I. C., Sarno, R., & Sholiq. (2020). Implementation of Random Forest Regression for COCOMO II Effort Estimation. 2020 International Seminar on Application for Technology of Information and Communication (ISemantic), 476–481. https://doi.org/10.1109/iSemantic50169.2020.9234269
Tahir, F., & Adil, M. (2018). An Empirical Analysis of Cost Estimation Models on Undergraduate Projects Using COCOMO II. 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 1–5. https://doi.org/10.1109/ICSCEE.2018.8538361
Yadav, R. (2017). OPTIMIZED MODEL FOR SOFTWARE EFFORT ESTIMATION USING COCOMO-2 METRICS WITH FUZZY LOGIC. International Journal of Advanced Research in Computer Science, 8, 121–125. https://doi.org/10.26483/ijarcs.v8i7.4113
Zhang, L. (2019). The Research on General Case-Based Reasoning Method Based on TF-IDF. 2019 2nd International Conference on Safety Produce Informatization (IICSPI), 670–673. https://doi.org/10.1109/IICSPI48186.2019.9095927
Published
2022-07-18
How to Cite
Feta, N. (2022). INTEGRATION OF FUZZY LOGIC METHOD AND COCOMO II ALGORITHM TO IMPROVE PREDICTION TIMELINESS AND SOFTWARE DEVELOPMENT COST. Jurnal Techno Nusa Mandiri, 19(1), 46 - 54. https://doi.org/10.33480/techno.v19i1.3037
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