Calibrating use case points using bayesian analysis

Background : Use Case Points (UCPs) have been widely used to estimate software size for object-oriented projects. Yet, many research papers criticize the UCPs methodology for not being verified and validated with data, leading to inaccurate size estimates. Aims : This paper explores the use of Bayesian analysis to calibrate the use case complexity weights of the UCPs method to improve software size and project effort estimation accuracy. Method : Bayesian analysis is applied to integrate prior information (in this study, the weights defined by the UCPs method and suggested by other research papers) with parameter values suggested by multiple linear regression on the data. To validate the effectiveness of this approach, we run the Bayesian-inspired analysis on projects implemented by master's students at University of Southern California and a public dataset retrieved from PROMISE, and compared its performance with three other typical size estimation methods: a priori, original UCPs, and regression methods. To test the approach in a heterogeneous environment, we also run the analysis on the combination of the student projects and the public dataset. Results : The Bayesian method outperforms the a priori, original UCPs, and regression methods by 13.4%, 15.9%, and 15.9% respectively in terms of PRED(.25), and by 16.8%, 16.9%, and 17.8% respectively in terms of MMRE for the student projects. The PRED(.25) and MMRE results similarly improved for the public and the combined datasets. Conclusions : The results show that the Bayesian estimates of the use case complexity weights consistently provide better estimation accuracy, compared to the weights proposed by the original UCPs method, the weights calibrated by multiple linear regression, and the weights suggested in previous research papers.