Abstract
Estimation of an effort is necessary to decide the time and cost needed for the completion of the project. Making judgments with a high degree of uncertainty is a serious issue in the field of software engineering. Software quality forecasting calls for highly precise technologies and expert knowledge. The forecast of software effort based on past data from software development metrics could, instead, be aided by AI-based predictive models, which have a high degree of accuracy. Using a linear regression model, we developed a software effort estimation model in this study to forecast this effort. A non-parametric linear regression approach based on K-Nearest Neighbors was used to create this statistical model (KNN). So, with a 76% coefficient of determination, our findings indicate the potential for applying AI algorithms to predict the software engineering work prediction problem.
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