Hybridizing Intelligence: A Comparative Study of Machine Learning Algorithm and ANN-PSO Deep Learning Model for Software Effort Estimation

doi:10.23940/ijpe.24.11.p3.668675

Abstract

Abstract: Software Effort Estimation (SEE) is a technique for properly anticipating staff during the development of a project. Software development process is complicated and the most critical step is estimating the staff effort required for the projects. Correctly identifying the precise quantity of effort required in the initial phases of development can be problematic. Researchers have developed many machine learning and deep learning algorithms to improve the accuracy. A hybrid model PSO (Particle swarm optimisation)-based artificial neural network (ANN) model for software effort estimating (SEE) is presented and has demonstrated to outperform traditional methods. This model was evaluated using several datasets, including China, Albrecht, Kitchenham, Desharnais, Maxwell, Kemerer and Cocomo81. The machine learning algorithms Bagging, Boosting Averaging, weighted Averaging, Stacking using RF specify algorithm, are widely used in real word applications for software development. Our experiments demonstrate that the hybrid ANN-PSO model outperforms the traditional machine learning algorithm in regards to accuracy, precision, and recall. The ANN-PSO hybrid model achieves an average accuracy compared to the ML algorithm. The results of this study highlight the potential of hybrid deep learning models in tackling complex problems, particularly those involving large datasets and high-dimensional feature spaces. The hybrid ANN-optimized PSO has shown exceptional accuracy, with consistently elevated R2-squared (R 2) values across several datasets. Furthermore, the model shows performance metrics RMSE and MAE values, implying reliable predictions. These results support the effectiveness and utility of the paradigm. The low MAE of the model indicates that it may predict software development task requirements with reasonable accuracy. Given these remarkable outcomes, the hybrid PSO-optimized ANNs model will surely be important in software development.

Key words: machine learning, software projects effort estimation, deep learning, optimization techniques

Meenakshi Chawla and Meenakshi Pareek. Hybridizing Intelligence: A Comparative Study of Machine Learning Algorithm and ANN-PSO Deep Learning Model for Software Effort Estimation [J]. Int J Performability Eng, 2024, 20(11): 668-675.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] AG P.V., K A.K., and Varadarajan V., 2021. Estimating software development efforts using a random forest-based stacked ensemble approach.Electronics, 10(10), 1195.
[2] Novitasari D., Cholissodin I., and Mahmudy W.F., 2016. Optimizing svr using local best pso for software effort estimation. Journal of Information Technology and Computer Science,1(1), pp. 28-37.
[3] Bilgaiyan S., Aditya K., Mishra S., and Das M., 2018. A swarm intelligence based chaotic morphological approach for software development cost estimation.International Journal of Intelligent Systems and Applications (IJISA), pp. 2074-9058.
[4] Khatibi Bardsiri V., Jawawi D.N.A., Hashim S.Z.M., and Khatibi E., 2013. A PSO-based model to increase the accuracy of software development effort estimation.Software Quality Journal, 21, pp. 501-526.
[5] Kumar A., Sinhal A., and Verma B., 2013. A novel technique of optimization for software metric using PSO. International Journal of Soft Computing And Software Engineering,3(10), pp. 2251-7545.
[6] Kalonia S., and Upadhyay A., 2023, September. A Systematic Review of Software Fault Prediction Using Deep Learning: Challenges and Future Perspectives. InInternational Conference on Advances in Data-driven Computing and Intelligent Systems, pp. 533-548.
[7] Dan Z.,2013, July. Improving the accuracy in software effort estimation: Using artificial neural network model based on particle swarm optimization. InProceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 180-185.
[8] Khatibi Bardsiri V., Jawawi D.N.A., Hashim S.Z.M., and Khatibi E., 2014. A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons.Empirical Software Engineering, 19, pp. 857-884.
[9] Khuat T.T., and Le M.H., 2018. A novel hybrid ABC-PSO algorithm for effort estimation of software projects using agile methodologies. Journal of Intelligent Systems,27(3), pp. 489-506.
[10] Shahpar Z., Bardsiri V.K., and Bardsiri A.K., 2021. Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing.Concurrency and Computation: Practice and Experience, 33(20), e6358.
[11] Khan M.S., Jabeen F., Ghouzali S., Rehman Z., Naz S., and Abdul W., 2021. Metaheuristic algorithms in optimizing deep neural network model for software effort estimation.Ieee Access, 9, pp. 60309-60327.
[12] Kassaymeh S., Alweshah M., Al-Betar M.A., Hammouri A.I., and Al-Ma’aitah M.A., 2024. Software effort estimation modelling and fully connected artificial neural network optimization using soft computing techniques. Cluster Computing,27(1), pp. 737-760.
[13] Kumar K.H., and Srinivas K., 2023. An accurate analogy based software effort estimation using hybrid optimization and machine learning techniques. Multimedia Tools and Applications,82(20), pp. 30463-30490.
[14] Marco R., Ahmad S.S.S., and Ahmad S., 2023. An Improving long short term memory-grid search based deep learning neural network for software effort estimation. International Journal of Intelligent Engineering & Systems,16(4).

[1]	Ashu Mehta, Navdeep Kaur, and Amandeep Kaur. A Review of Software Fault Prediction Techniques in Class Imbalance Scenarios [J]. Int J Performability Eng, 2025, 21(3): 123-130.
[2]	Vikas, Charu Wahi, Bharat Bhushan Sagar, and Manisha Manjul. Trust Management in WSN using ML for Detection of DDoS Attacks [J]. Int J Performability Eng, 2025, 21(3): 157-167.
[3]	Pancham Singh, Updesh Kumar Jaiswal, Eshank Jain, Nikhil Kumar, and Vimlesh Mishra. A Novel Methodology Utilizing Modern CCTV Cameras and Software as a Service Model for Crime Detection and Prediction [J]. Int J Performability Eng, 2025, 21(2): 112-121.
[4]	Arpna Saxena and Sangeeta Mittal. CluSHAPify: Synergizing Clustering and SHAP Value Interpretations for Improved Reconnaissance Attack Detection in IIoT Networks [J]. Int J Performability Eng, 2025, 21(1): 36-47.
[5]	Seema Kalonia and Amrita Upadhyay. Comparative Analysis of Machine Learning Model and PSO Optimized CNN-RNN for Software Fault Prediction [J]. Int J Performability Eng, 2025, 21(1): 48-55.
[6]	Manu Banga. Enhancing Software Fault Prediction using Machine Learning [J]. Int J Performability Eng, 2024, 20(9): 529-540.
[7]	Vikas Kumar, Charu Wahi, Bharat Bhushan Sagar, and Manisha Manjul. Ensemble Learning Based Intrusion Detection for Wireless Sensor Network Environment [J]. Int J Performability Eng, 2024, 20(9): 541-551.
[8]	Aditya Dayal Tyagi, and Krishna Asawa. Influence Maximization in Social Network using Community Detection and Node Modularity [J]. Int J Performability Eng, 2024, 20(9): 552-562.
[9]	Ekta Singh, and Parma Nand. Efficient Multi-Class Facial Emotion Recognition using YOLOv9: A Deep Learning Approach for Real-Time Applications [J]. Int J Performability Eng, 2024, 20(9): 581-590.
[10]	Manpreet Singh, Gauri Jindal, Akshita Oberoi, and Rohan Dhangar. Improving Crime Detection Through Geo-MDA: A Hybrid Linear Regression Approach in Data Mining [J]. Int J Performability Eng, 2024, 20(8): 469-477.
[11]	Nilesh Shelke, Deepali Sale, Sagar Shinde, Atul Kathole, and Rachna Somkunwar. A Comprehensive Framework for Facial Emotion Detection using Deep Learning [J]. Int J Performability Eng, 2024, 20(8): 487-497.
[12]	Kalyani H. Deshmukh, Gajendra R. Bamnote, and Pratik K Agrawal. A Novel Approach for Drought Monitoring and Evaluation using Time Series Analysis and Deep Learning [J]. Int J Performability Eng, 2024, 20(8): 498-509.
[13]	Saurabh Saxena, and Chetna Gupta. Optimizing Bug Resolution: A Data-Driven Developer Recommendation System [J]. Int J Performability Eng, 2024, 20(8): 510-519.
[14]	Lakshya Vaswani, Sai Sri Harsha, Subham Jaiswal, and Aju D. Unravelling Complexity: Investigating the Effectiveness of SHAP Algorithm for Improving Explainability in Network Intrusion System Across Machine and Deep Learning Models [J]. Int J Performability Eng, 2024, 20(7): 421-431.
[15]	Meenakshi Chawla and Meenakshi Pareek. A Hybrid Deep Learning Perspective for Software Effort Estimation [J]. Int J Performability Eng, 2024, 20(7): 442-450.