Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
- Title
- Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
- Creator
- Shoran P.; Sinha A.; Mahmood H.R.; Sharma V.; Jha P.; Kumar B.; Alkhayyat A.
- Description
- This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE.
- Source
- 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Battle Royale Optimization; BRO; COCOMO model; cost drivers; cost estimation; machine learning; QEMRT; Quantum Ensemble Meta-Regression Technique; software
- Coverage
- Shoran P., Dr. Akhilesh das Gupta Institure of Technology and Management, India; Sinha A., Ignou, Department of Cs and It, New Delhi, India; Mahmood H.R., Fast Nuces Cfd Campus, Punjab, Pakistan; Sharma V., Christ University, Department of Computer Science, Delhi NCR, India; Jha P., Amity University Jhrakhand, Department of Computer Science An Engineering, Ranchi, India; Kumar B., Amity University Jhrakhand, Department of Computer Science An Engineering, Ranchi, India; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
- Rights
- Restricted Access
- Relation
- ISBN: 979-835033509-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Shoran P.; Sinha A.; Mahmood H.R.; Sharma V.; Jha P.; Kumar B.; Alkhayyat A., “Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/19757.