A Hybrid Cost Estimation Method for Planning Software Projects Using Fuzzy Logic and Machine Learning
Keywords:
Software, Cost, Estimation, Project, Machine Learning (ML), Fuzzy LogicAbstract
Accurate cost prediction is crucial for efficient project management due to the potential for delays and budget over runs. The present research presents a novel method for accurately estimating the price of software projects by combining fuzzy logic with machine learning. The technique incorporates pre-processing procedures, feature selection, and fuzzy rule building to make use of historical data from datasets including "Desharnais," "Kitchenham," and "Maxwell." An ensemble classifier is built up out of several methods (such as Linear Regression (LR), Support Vector Machine (SVM), Feed Forward Neural Network (FNN), and Recurrent Neural Networks (RNN)) and then evaluated using various standards. Prominent results include the SVM model achieving the highest R-squared error in the Desharnais dataset, whereas the Ensemble model excelled in the Kitchenham dataset, achieving the highest R-squared error at 0.9307893, and the lowest Root Mean Squared Error at 0.2707119 among all models. In the Maxwell dataset, the LR model had the maximum R-squared error of 0.6073169, while the RNN model had the lowest R-squared error of 0.0237821. This method has the potential to improve software project planning in the actual world through accurate cost estimation.
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