Ensemble Learning Approach for Effective Software Development Effort Estimation with Future Ranking
Abstract To provide a client with a high-quality product, software development requires a significant amount of time and effort. Accurate estimates and on-time delivery are requirements for the software industry. The proper effort, resources, time, and schedule needed to complete a software project on a tight budget are estimated by software development effort estimation. To achieve high levels of accuracy and effectiveness while using fewer resources, project managers are improving their use of a model created to evaluate software development efforts properly as a decision-support system. As a result, this paper proposed that a novel model capable of determining precise accuracy of global and large-scale software products be developed with practical efforts. The primary goal of this paper is to develop and apply a practical ensemble approach for predicting software development effort. There are two parts to this study: the first phase uses machine learning models to extract the most useful features from previous studies. The development effort is calculated in the second phase using an advanced ensemble method based on the components of the first phase. The performance of the developed model outperformed the existing models after a controlled experiment was conducted to develop an ensemble model, evaluate it, and tune its parameters.
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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, 137, 184–196. 10.1016/j.jss.2017.11.066
Rani, P.; Kumar, R.; Jain, A.; Chawla, S. K., 2021. A hybrid approach for feature selection based on genetic algorithm and recursive feature elimination. International Journal of Information System Modeling and Design (IJISMD), 12(2), 17–38. 10.4018/IJISMD.2021040102
Rao, K. E.; Rao, G. A., 2021. Ensemble learning with recursive feature elimination integrated software effort estimation: a novel approach. Evolutionary Intelligence, 14(1), 151–162. 10.1007/s12065-020-00360-5
Rijwani, P.; Jain, S., 2016. Enhanced software effort estimation using multi layered feed forward artificial neural network technique. Procedia Computer Science, 89, 307–312. 10.1016/j.procs.2016.06.073
Rosen, C. 2020. Guide to Software Systems Development-Connecting Novel Theory and Current Practice. Springer International Publishing. 10.1007/978-3-030-39730-2
Shah, J.; Kama, N., 2018, February. Extending function point analysis effort estimation method for software development phase. In Proceedings of the 2018 7th International Conference on Software and Computer Applications (pp. 77–81). 10.1145/3185089.3185137
Singh, C.; Sharma, N.; Kumar, N., 2019. An Efficient Approach for Software Maintenance Effort Estimation Using Particle Swarm Optimization Technique. International Journal of Recent Technology and Engineering (IJRTE), 7(6C).
V.V., Hai, H.L.T.K., Nhung, Z., Prokopova, R., Silhavy, P., Silhavy, 2021. A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation. MDPI-Computers, 11, 2. 10.3390/computers11020015
Azath, H.; Amudhavalli, P.; Rajalakshmi, S.; Marikannan, M., 2018. A novel regression neural network based optimized algorithm for software development cost and effort estimation. J. Web Eng, 17(6), 3095–3125.
Chiang, H. Y.; Lin, B. M. T., 2020. A Decision Model for Human Resource Allocation in Project Management of Software Development. IEEE Access, 8, 38073–38081. 10.1109/ACCESS.2020.2975829
De Carvalho, H. D. P. Fagundes, R.; Santos, W., 2021. Extreme Learning Machine Applied to Software Development Effort Estimation. IEEE Access, 9, 92676–92687. 10.1109/ACCESS.2021.3091313
Denard, S.; Ertas, A.; Mengel, S.; Osire, S. E., 2020. Development Cycle Modeling: Resource Estimation. MDPI-applied Science, 10, 5013. 10.3390/app10145013
Dewi, R. S.; Subriadi, A. P., 2017. A comparative study of software development size estimation method: UCPabc vs function points. Procedia Computer Science, 124, 470–477. 10.1016/j.procs.2017.12.179
Diwaker, C.; Tomar, P.; Poonia, R. C.; Singh, V. (2018). Prediction of software reliability using bio inspired soft computing techniques. Journal of medical systems, 42(5), 1–16. 10.1007/s10916-018-0952-3
El Bajta. M.; Idri, A., 2020. Identifying Software Cost Attributes of Software Project Management in Global Software Development: An Integrative Framework. ACM-Digital Library, 39, 1–5. 10.1145/3419604.3419780
Ghatasheh, N.; Faris, H.; Aljarah, I.; Al-Sayyed, R. M., 2019. Optimizing software effort estimation models using firefly algorithm. arXiv preprint arXiv:1903.02079.
Hai, V. V.; Nhung, H. L. T. K., Prokopova, Z.; Silhavy, R.; Silhavy, P., 2021. A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation. MDPI-Computers, 11(2). 10.3390/computers11020015
Hasan, M. A. M.; Nasser, M.; Ahmad, S.; Molla, K. I. (2016). Feature selection for intrusion detection using random forest. Journal of information security, 7(3), 129–140. 10.4236/jis.2016.73009
Idri, A.; Abran, A.; Khoshgoftaar, T. M., 2002, June. Estimating software project effort by analogy based on linguistic values. Proceedings Eighth IEEE Symposium on Software Metrics (pp. 21–30). IEEE
Jing, X. Y.; Qi, F.; Wu, F.; Xu, B., 2016, May. Missing data imputation based on low-rank recovery and semi-supervised regression for software effort estimation. 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE) (pp. 607–618). IEEE. 10.1145/2884781.2884827
Kuhail; M. A.; Lauesen, S., 2022. User Story Quality in Practice: A Case Study. MDPI-Software, 1, 223–243. 10.3390/software1030010
Kumar, K.; Aihole, S.; Putage, S., 2017. Anticipation of software development effort using artificial neural network for NASA data sets. Int J Eng Sci, 7(5), 11228.
Mak, L.; Taheri, P., 2022. An Automated Tool for Upgrading Fortran Codes. MDPI-Software, 1, 299–315, 2022. doi: 10.3390/software1030014
Mashkoor, A.; Menzies, T.; Egyed, A.; Ramler, R., 2022. Artificial Intelligence and Software Engineering: Are We Ready? Computer, 55(3), 24–28. 10.1109/MC.2022.3144805
Mensah, S.; Keung, J.; Bosu, M. F.; Bennin, K. E., 2018. Duplex output software effort estimation model with self-guided interpretation. Information and Software Technology, 94, 1–13. 10.1016/j.infsof.2017.09.010
Menzies, T.; Yang, Y.; Mathew, G.; Boehm, B.; Hihn, J., 2017. Negative results for software effort estimation. Empirical Software Engineering, 22(5), 2658–2683. 10.1007/s10664-016-9472-2
Mustapha, H.; Abdelwahed, N., 2019. Investigating the use of random forest in software effort estimation. Procedia computer science, 148, 343–352. 10.1016/j.procs.2019.01.042
Nassif, A. B.; Azzeh, M.; Idri, A.; Abran, A., 2019. Software development effort estimation using regression fuzzy models. Computational intelligence and neuroscience. 10.1155/2019/8367214
Phannachitta, P.; Keung, J.; Monden, A.; Matsumoto, K., 2017. A stability assessment of solution adaptation techniques for analogy-based software effort estimation. Empirical Software Engineering, 22(1), 474–504. 10.1007/s10664-016-9434-8
Pillai, K.; Jeyakumar, M., 2019. A real time extreme learning machine for software development effort estimation. Int. Arab J. Inf. Technol., 16(1), 17–22.
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, 137, 184–196. 10.1016/j.jss.2017.11.066
Rani, P.; Kumar, R.; Jain, A.; Chawla, S. K., 2021. A hybrid approach for feature selection based on genetic algorithm and recursive feature elimination. International Journal of Information System Modeling and Design (IJISMD), 12(2), 17–38. 10.4018/IJISMD.2021040102
Rao, K. E.; Rao, G. A., 2021. Ensemble learning with recursive feature elimination integrated software effort estimation: a novel approach. Evolutionary Intelligence, 14(1), 151–162. 10.1007/s12065-020-00360-5
Rijwani, P.; Jain, S., 2016. Enhanced software effort estimation using multi layered feed forward artificial neural network technique. Procedia Computer Science, 89, 307–312. 10.1016/j.procs.2016.06.073
Rosen, C. 2020. Guide to Software Systems Development-Connecting Novel Theory and Current Practice. Springer International Publishing. 10.1007/978-3-030-39730-2
Shah, J.; Kama, N., 2018, February. Extending function point analysis effort estimation method for software development phase. In Proceedings of the 2018 7th International Conference on Software and Computer Applications (pp. 77–81). 10.1145/3185089.3185137
Singh, C.; Sharma, N.; Kumar, N., 2019. An Efficient Approach for Software Maintenance Effort Estimation Using Particle Swarm Optimization Technique. International Journal of Recent Technology and Engineering (IJRTE), 7(6C).
V.V., Hai, H.L.T.K., Nhung, Z., Prokopova, R., Silhavy, P., Silhavy, 2021. A New Approach to Calibrating Functional Complexity Weight in Software Development Effort Estimation. MDPI-Computers, 11, 2. 10.3390/computers11020015
Eswara Rao, K., Pydi, B., Annan Naidu, P., Prasann, U. D., & Anjaneyulu, P. (2023). Ensemble Learning Approach for Effective Software Development Effort Estimation with Future Ranking. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e31206. https://doi.org/10.14201/adcaij.31206
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