Iterative Feature Elimination Method Using Artificial Neural Network for Software Effort Estimation

Iterative Feature Elimination Method Using Artificial Neural Network for Software Effort Estimation
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© 2024 by IJETT Journal
Volume-72 Issue-2
Year of Publication : 2024
Author : Pranay Tandon, Ugrasen Suman
DOI : 10.14445/22315381/IJETT-V72I2P102

How to Cite?

Pranay Tandon, Ugrasen Suman, "Iterative Feature Elimination Method Using Artificial Neural Network for Software Effort Estimation," International Journal of Engineering Trends and Technology, vol. 72, no. 2, pp. 9-18, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I2P102

Abstract
Effort estimation is one of the critical tasks for any software development team because estimation is the key to planning the software development life cycle activities with proper timeline and cost. On-time and quality delivery is most important to build customer trust and certainty. There are many features to be considered while estimating the efforts, but removing the weak features and finding the set of the strongest features for any estimation process is difficult. Deep learning is the most popular prediction technique for effort estimation because of its capacity to adapt and be accurate on different types of datasets. Artificial Neural Network is best suited to deep learning techniques for predicting effort, per industrial research. In this paper, a novel model based on artificial neural networks and an iterative feature elimination-based method has been proposed to estimate the efforts. With ranking features, the proposed method can find the optimized set of features to be used in the model and final efforts. COCOMO NASA 2 dataset is used to find the results.

Keywords
Iterative feature elimination, Artificial Neural Network, Software effort estimation, Machine Learning, Deep learning.

References
[1] Kayhan Moharreri et al., “Cost-Effective Supervised Learning Models for Software Effort Estimation in Agile Environments,” IEEE 40th Annual Computer Software and Applications Conference, Atlanta, GA, USA, pp. 135-140, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ahmed BaniMustafa, “Predicting Software Effort Estimation Using Machine Learning Techniques,” 2018 8 th International Conference on Computer Science and Information Technology, Amman, Jordan, pp. 249-256, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Vlad-Sebastian Ionescu, Horia Demian, and Istvan-Gergely Czibula, “Natural Language Processing and Machine Learning Methods for Software Development Effort Estimation,” Studies in Informatics and Control, vol. 26, no. 2, pp. 219-228, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Shashank Mouli Satapathy, and Santanu Kumar Rath, “Empirical Assessment of Machine Learning Models for Agile Software Development Effort Estimation Using Story Points,” Innovations in Systems and Software Engineering, vol. 13, pp. 191-200, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Laura-Diana Radu, “Effort Prediction in Agile Software Development with Bayesian Networks,” Proceedings of the 14th International Conference on Software Technologies, Prague, Czech, vol. 1, pp. 238-245, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Hosahalli Mahalingappa Premalatha, and Chimanahalli Venkateshavittalachar Srikrishna, “Effort Estimation in Agile Software Development Using Evolutionary Cost-Sensitive Deep Belief Network,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 2, pp. 261-269, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Saurabh Bilgaiyan, Samaresh Mishra, and Madhabananda Das, “Effort Estimation in Agile Software Development Using Experimental Validation of Neural Network Models,” International Journal of Information Technology, vol. 11, pp. 569-573, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Morakot Choetkiertikul et al., “A Deep Learning Model for Estimating Story Points,” IEEE Transactions on Software Engineering, vol. 45, no. 7, pp. 637-656, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Passakorn Phannachitta, and Kenichi Matsumoto, “Model-Based Software Effort Estimation–A Robust Comparison of 14 Algorithms Widely Used in the Data Science Community,” International Journal of Innovative Computing, Information and Control, vol. 15, no. 2, pp. 569-589, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Onkar Malgonde, and Kaushal Chari, “An Ensemble-Based Model for Predicting Agile Software Development Effort,” Empirical Software Engineering, vol. 24, pp. 1017-1055, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Przemyslaw Pospieszny, Beata Czarnacka-Chrobot, and Andrzej Kobylinski, “An Effective Approach for Software Project Effort and Duration Estimation with Machine Learning Algorithms,” Journal of Systems and Software, vol. 137, pp. 184-196, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Serpil Ustebay, Zeynep Turgut, and Muhammed Ali Aydin, “Intrusion Detection System with Recursive Feature Elimination by Using Random Forest and Deep Learning Classifier,” International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism, Ankara, Turkey, pp. 71-76, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Puneet Misra, and Arun Singh Yadav, “Improving the Classification Accuracy Using Recursive Feature Elimination with CrossValidation,” International Journal on Emerging Technologies, vol. 11, no. 3, pp. 659-665, 2020.
 [Google Scholar] [Publisher Link]
[14] Neha V. Sharma, and Narendra Singh Yadav, “An Optimal Intrusion Detection System Using Recursive Feature Elimination and Ensemble of Classifiers,” Microprocessors and Microsystems, vol. 85, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] K. Eswara Rao, and G. Appa Rao, “Ensemble Learning with Recursive Feature Elimination Integrated Software Effort Estimation: A Novel Approach,” Evolutionary Intelligence, vol. 14, no. 1, pp. 151-162, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Bhaskar Marapelli, “Software Development Effort Duration and Cost Estimation Using Linear Regression and K-Nearest Neighbors Machine Learning Algorithms,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 2, pp. 1043-1047, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Srdjana Dragicevic, Stipe Celar, and Mili Turic, “Bayesian Network Model for Task Effort Estimation in Agile Software Development,” Journal of Systems and Software, vol. 127, pp. 109-119, 2017.
[CrossRef] [Google Scholar] [Publisher Link]