A Deep Learning-Based Technique for Evaluation of Estimation in Software Development

Precise project estimation is essential to successful software development, as is resource allocation, planning, and project management as a whole. Traditional estimation methods usually rely on historical data and expert judgment, both of which may contain errors. This paper proposes a novel approach to enhance the evaluation of estimation in software development by utilizing cutting-edge deep learning algorithms. The proposed method comprises collecting and preparing historical project data (initial estimates and actual work performed) for software development projects. We take the relevant characteristics—like project size, complexity, team experience, and requirement fluctuations—and translate them into numerical forms. To capture complicated correlations in the data, neural network architecture with multiple hidden layers is designed specifically for regression tasks. To train the model, we split the dataset into distinct training and validation sets during the training phase. During training, an appropriate loss function and optimization strategy is used. Cross-validation and hyper parameter tuning are used to optimize the model's performance and ensure its robustness. Next, we validate the trained model on a different dataset and assess how well it performs project estimation in a real-world scenario. Creating a user-friendly interface or integrating the approach into existing project management systems are two ways to make it easier to use in practice. Update the model often with new data to facilitate continual learning.