Optimizing Software Cost Estimation: A Hybrid Approach Integrating Soft Computing Techniques for Enhanced Accuracy and Efficiency

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In software engineering, software cost estimate is a necessary process directly affecting project deadlines, budget planning, and resource allocation. Conventional estimating methods could overlook the inherent complexity and uncertainty of software development, therefore causing mistakes and maybe project failures. To optimise software cost estimation and so address these challenges, this work proposes a hybrid framework incorporating fuzzy logic, artificial neural networks (ANN), and genetic algorithms (GA). Although fuzzy logic models imprecise and ambiguous information to control uncertainty, ANN is employed for its ability to learn and generalise patterns from past data. GA guarantees accurate and effective assessment of the process by strengthening already used techniques with strong optimising characteristics. Combining these techniques makes use of their respective benefits to generate a synergistic plan beyond more traditional methods. The proposed framework is investigated using industry-standard datasets exhibiting appreciable increases in prediction accuracy, computational efficiency, and flexibility to varied project situations. Comparatively, for complex and large-scale projects the hybrid approach not only reduces estimating errors but also enhances scalability and dependability. Additionally included in the paper is a case application for a practical project, therefore stressing the relevant usefulness of the framework. By addressing the limitations of present approaches, this work reduces the gap between theoretical breakthroughs in soft computing and their pragmatic use in software engineering. stressing the need of applying contemporary computing techniques to solve evolving challenges in cost estimation, the outcomes provide useful information for scholars and business practitioners. At the end of this work is explored the possibility of new machine learning models and the investigation of domain-specific customisations to increase the applicability of the framework.