Modeling software development effort estimation models has been a hot research topic over the last
three decades. Numerous models were proposed in these decades to predict the effort. The key challenges for future
software development is providing accurate software estimation. Failure to acknowledge the accuracy of effort
estimation can cause inaccurate estimation, customer disappointment, and poor software development or project
failure. This research presents a novel computational technique, named adaptive neuro-fuzzy inference system
(ANFIS), for the modeling of software effort estimation. It was developed utilizing the Constructive Cost Model
(COCOMO) dataset. The data were randomly divided into two sets: 83% for training and 17% for testing. The mean
magnitude relative-error (MMRE) and the coefficient of correlation (R) were used as assessment indices. Results
showed that the accuracy of the proposed model is quite satisfactory in comparison with actual values. Moreover, a
comparison study was conducted with another approach. The results showed that ANFIS produced better results in
comparison with Function Point Analysis, Software Lifecycle Management, and COCOMO methods. ANFIS was
found to be a potential predictive model for software development effort estimation.