The COCOMO II model is an algorithmic approach to estimating software development effort. COCOMO II uses size and numerical input measures regarding application points multiplied by constants that are empirically determined constants to provide estimations (Ivan & Despa, 2016). The use of company-specific calibration and historical data increase accuracy (Moharreri, Sapre, Ramanathan, & Ramnath, 2016). The COCOMO II model has the advantages of objectivity, repeatability, built-in sensitivity to development factors, and model calibration to previous projects and experiences (Osmanbegović et al., 2017). COCOMO II uses multiple factors for calibration and is most effective when using historical data.
The effectiveness of the algorithmic COCOMO II estimation model’s effectiveness model relies on historical data to provide accurate estimations. Estimators calibrate the model using factors such as flexibility of the development, team cohesion, reuse, architecture, risk, platform experience, database size, the volatility of the platform, personnel continuity and experience, time constraints, complexity, and team capability (Boehm et al., 2000). An advantage of COCOMO II is that modification and customization of the model are straightforward (Prakash & Viswanathan, 2017). However, Prakash and Viswanathan (2017) also stated that the method becomes much less effective if historical data is not available. Additionally, the unavailable. The COCOMO II model is more suited to a procedural development paradigm than the agile development model (Kukreja & Garg, 2017; Rath et al., 2016).