An Efficient Framework for Software Maintenance Cost Estimation Using Genetic Hybrid Algorithm: OOPs Prospective

Abstract

One of the most significant exercises in software development is the software cost estimation, which leads to improvements in software engineering technique. The objectives of cost estimation, including effort, schedule, and manpower needs, are helpful advice for the establishment and operation of projects. This paper proposes an object-oriented software development framework for maintenance cost estimation using a genetic hybrid algorithm, a novel approach for estimating the maintenance cost of software systems. The framework combines object-oriented software development principles with genetic algorithm techniques to create a hybrid algorithm that can accurately estimate maintenance costs for software projects. The paper begins by discussing the importance of accurately estimating maintenance costs, as software maintenance can account for up to 60% of the total cost of a software system. Then the paper outlines the proposed framework, which consists of several components, including a cost estimation model and a genetic algorithm. The cost estimation model uses a set of parameters to predict maintenance costs, and the genetic algorithm is used to optimize the model’s parameters for maximum accuracy using an appropriate data set. The paper then presents the results of an empirical study that was conducted to evaluate the effectiveness of the proposed framework. The study found that the framework was able to accurately estimate maintenance costs for several software projects by reducing root mean square error (RMSE) as well as mean absolute error (MAE). It has been observed that an improved prediction model over the regression model has been developed, resulting in lower RMSE and MAE values of 61.66 and 0.098818, respectively, as compared to the earlier ones from the regression model of 96.31 and 0.1718818, respectively. Overall, the Object-Oriented Software Development Framework for Maintenance Cost Estimation using Genetic Hybrid Algorithm Techniques provides a promising approach for accurately estimating maintenance costs for software systems, which can help organizations better manage their software development projects and budgets.