An adaptive anti-predatory algorithm-based model to enhance the efficiency of software effort estimation

Effort estimation is crucial in the early stage of the software development life cycle. Inaccurate estimation often leads to project failures, which is a pervasive issue nowadays for software project managers. For the software’s high performance, well-known estimating methods such as the Constructive Cost Model (COCOMO) need improvement in terms of parameter optimization. The objective of this work is to develop an effective framework that refines the parameters of COCOMO II model aiming to predict and improve estimation accuracy. We proposed an improved parameters tuning method for COCOMO II using a novel metaheuristic adaptive memetic improved anti-predatory nature-inspired algorithm (Ada-MIAPNIA). The algorithm adapts weight modifications through Lévy flight-inspired motions, enhancing global search effectiveness and optimizing the equilibrium between exploratory and exploitative approaches. Furthermore the proposed algorithm is also incorporating elitism, to ensures the retention of the optimal solution throughout each optimization stage. The effectiveness of the Ada-MIAPNIA was rigorously evaluated using 31 benchmark functions, with its performance validated through statistical tests. The experimental findings demonstrate that the proposed Ada-MIAPNIA outperforms other counterpart nature-inspired algorithms (NIAs). The proposed framework’s performance is further solidified through an evaluation using NASA software project datasets, which reinforces the algorithm’s efficacy. The results were validated using evaluation criteria such as Mean Magnitude of Relative Error (MMRE) and prediction (0.25). Ada-MIAPNIA outperforms the existing COCOMO II model and other nature-inspired algorithms (NIAs) by substantial margins, ranging from 2.02 to 31.94% across different datasets and algorithms.