Satin bowerbird optimizer: A new optimization algorithm to optimize ANFIS for software development effort estimation

Accurate software development effort estimation is crucial to efficient planning of software projects. Due to complex nature of software projects, development effort estimation has become a challenging issue which must be seriously considered at the early stages of project. Insufficient information and uncertain requirements are the main reasons behind unreliable estimations in this area. Although numerous effort estimation models have been proposed during the last decade, accuracy level is not satisfying enough. This paper presents a new model based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and satin bower bird optimization algorithm (SBO) to reach more accurate software development effort estimations. SBO is a novel optimization algorithm proposed to adjust the components of ANFIS through applying small and reasonable changes in variables. The proposed hybrid model is an optimized neuro-fuzzy based estimation model which is capable of producing accurate estimations in a wide range of software projects. The proposed optimization algorithm is compared against other bio inspired optimization algorithms using 13 standard test functions including unimodal and multimodal functions. Moreover, the proposed hybrid model is evaluated using three real data sets. Results show that the proposed model can significantly improve the performance metrics.