Swarm Intelligence-Based Functional Link Fuzzy Neural Estimator for Software Development Effort Estimation

Tirimula Rao Benala; Anupama Kaushik; Satchidananda Dehuri

doi:10.35882/jeeemi.v7i2.492

Tirimula Rao Benala Department of Information Technology, JNTU-GV College of Engineering Vizianagaram (Autonomous), Jawaharlal Nehru Technological University Gurajada Vizianagaram Dwarapudi Vizianagaram, Andhra Pradesh-535003, India https://orcid.org/0000-0002-0613-9893
Anupama Kaushik Department of IT, Maharaja Surajmal Institute of Technology, Affiliated to GGSIP University, New Delhi, India https://orcid.org/0000-0003-4665-1434
Satchidananda Dehuri Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore-756019, Odisha, India https://orcid.org/0000-0003-1435-4531

DOI: https://doi.org/10.35882/jeeemi.v7i2.492

Keywords: Software Cost Estimation, Functional Link Artificial Neural Network, Fuzzy Logic System, Interval Type-2FLS, Particle Swarm Optimization, Active Learning Algorithm

Abstract

Accurate Software Development Effort Estimation (SDEE) is pivotal for effective project management, significantly impacting resource allocation and the overall success of software projects. This paper introduces the Swarm Intelligence-Based Functional Link Fuzzy Neural Estimator (SFNE), a novel computational intelligence model designed to enhance estimation accuracy by integrating multiple advanced methodologies. The SFNE framework employs the QUICK algorithm for dataset optimization, effectively minimizing noise and redundancy. A Functional Link Artificial Neural Network (FLANN) captures complex nonlinear relationships within the data, while Interval Type-2 Fuzzy Logic Systems (IT2FLS) address inherent data uncertainties. Additionally, Particle Swarm Optimization (PSO) is applied to fine-tune model parameters, improving prediction precision. Empirical evaluations were conducted using six benchmark datasets from the PROMISE repository. The results demonstrate that the SFNE model significantly outperforms existing models across key metrics, including Mean Magnitude of Relative Error (MMRE), Median Magnitude of Relative Error (MdMRE), and Prediction at 0.25 (PRED(0.25)). Notably, SFNE achieved a predictive accuracy of 99.983% on the DesharnaisL3 dataset and an MMRE of 2.87×10⁻⁵ on the DesharnaisL1 dataset. These findings underscore the robustness and adaptability of SFNE in addressing the limitations of traditional SDEE methods, particularly in managing data scarcity and uncertainty. The proposed SFNE model establishes a new benchmark for SDEE accuracy and demonstrates substantial potential for practical application in real-world software engineering projects. Future research will explore integrating additional computational intelligence techniques, such as deep learning and reinforcement learning, and developing automated tools to advance SDEE practices further. These advancements contribute to more reliable and efficient software project management, facilitating real-time effort estimation and informed decision-making in the software industry.

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