Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive Cost Model) is a parametric data-driven SDEE model whose parameters must be calibrated with an organization's local data for accurate estimation. Such data are scarce for most organizations. On the other hand, CoBRA (Cost estimation, Benchmarking, and Risk Assessment) is one of the powerful hybrid methods that need a small number of local historical data for effort estimation. However, data gathering in CoBRA is time-consuming and costly. To ease the use of CoBRA, in this paper, we design a methodology that extracts CoBRA-required data from COCOMO datasets. By the proposed method, data collected for COCOMO would be used in CoBRA. Using CoBRA, a more accurate estimation of the required effort would be achieved with fewer number of historical data than what is required to calibrate the COCOMO model. We apply the proposed method on six well-known public COCOMO datasets and use them in CoBRA. Obtained results depict an increase in the accuracy of estimations in comparison with other existing methods.