So, it can be used with deep neural networks to minimize training delays. Additionally, heuristic approaches allow for the location of an optimum solution. e meta-heuristic algorithm supports nding a good optimal solution at a reasonable computational cost. Deep learning models usually require ne-tuning to a large number of parameters. Although deep learning architectures provide some improvements over existing at technologies, they also have some shortcomings, such as large training delays, over-tting, and under-tting. e research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. erefore, for accurate estimation, it is necessary to ne-tune the coe cients. e limitation of the COCOMO models is that the values of these coe cients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization.
#Basic cocomo mmodel program in c software#
e constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. e rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. E ective software cost estimation signi cantly contributes to decision-making.
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