An Optimized Adaptive Neuro-Fuzzy Inference System to Estimate Software Development Effort

Abstract

One of the most critical activities in software project management during the project inception phase is to estimate the effort and cost needed to complete the project tasks. 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 firefly algorithm (FA) to reach more accurate software effort estimations. The proposed hybrid model is an optimized neuro-fuzzy based estimation model which is capable of producing accurate estimations. The proposed model is evaluated using three real data sets (ISBSG, Kemerer and Albrecht). Results show that the proposed model can significantly improve the performance metrics.