An empirical validation of four software cost estimation models (SLIM, COCOMO, Function Points, and ESTIMACS) was conducted using data from 15 large business data-processing projects. The study aimed to evaluate the accuracy of these models in estimating ex post effort. Key findings include: Albrecht's Function Points model was validated by the independent data, while models not developed in business data-processing environments required calibration. All models failed to sufficiently reflect underlying productivity factors. The Function Points model performed better than SLOC-based models, and non-proprietary models like Function Points were as accurate as proprietary models. The study highlights the need for calibration and the importance of using historical data to adapt models to specific environments. Results show that Function Points and ESTIMACS had lower error rates, while COCOMO and SLIM had higher errors. The study also notes that Function Points can predict KSLOC accurately, and that the accuracy of models depends on the environment. The research underscores the importance of calibration and the limitations of existing models in accurately estimating software development costs.An empirical validation of four software cost estimation models (SLIM, COCOMO, Function Points, and ESTIMACS) was conducted using data from 15 large business data-processing projects. The study aimed to evaluate the accuracy of these models in estimating ex post effort. Key findings include: Albrecht's Function Points model was validated by the independent data, while models not developed in business data-processing environments required calibration. All models failed to sufficiently reflect underlying productivity factors. The Function Points model performed better than SLOC-based models, and non-proprietary models like Function Points were as accurate as proprietary models. The study highlights the need for calibration and the importance of using historical data to adapt models to specific environments. Results show that Function Points and ESTIMACS had lower error rates, while COCOMO and SLIM had higher errors. The study also notes that Function Points can predict KSLOC accurately, and that the accuracy of models depends on the environment. The research underscores the importance of calibration and the limitations of existing models in accurately estimating software development costs.