TSoptEE: two-stage optimization technique for software development effort estimation

Accurate software development effort estimation (SDEE) is vital for effective project planning. Due to the complex nature of software projects, estimating development effort has become a challenging task that requires careful consideration, especially in the early project phases, to prevent overestimation and underestimation. Accurate effort estimation helps to estimate the cost of a developing project through effective resource management and project budgeting for manpower. Despite numerous effort estimation models introduced over the past two decades, achieving a satisfactory level of accuracy remains elusive. The adaptive neuro-fuzzy inference system (ANFIS) model gains more popularity for estimation tasks due to its rapid learning capacity, ability to represent complex nonlinear structures, and adaptability to improperly specified data. This study presents a model called the Two-Stage optimization technique for Software Development Effort Estimation (TSoptEE). Initially, it performs feature selection through a multi-objective improved binary social network search (SNS) algorithm and then optimizes the ANFIS tunable parameters through an improved SNS algorithm to enhance the accuracy of SDEE. The proposed TSoptEE model is compared against existing estimation models and evaluated using seven performance measures over nine software datasets. The obtained results are promising in terms of accuracy and statistical significance tests. This implies that the proposed model can significantly enhance the accuracy of effort estimation.