Abstract
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.
Highlights
In software project management, the estimation of software development efforts is an important topic
The adaptive neuro-fuzzy inference system (ANFIS) was developed to serve as a basis for constructing a fuzzy inference system (FIS), and its structure was obtained by incorporating fuzzy inference system into the artificial neural networks (ANNs) framework [19]
ANFIS models can be composed of various structures that govern output and estimation performance
Summary
The estimation of software development efforts is an important topic. Soft computing includes some effective methods utilizing the human tolerance for uncertainty, imprecision, approximation, and partial truth in decision making for solving problems under uncertainty or complex problems where available information is imprecise [8] Using these approaches would save a significant amount of time and cost and improve accuracy. To the best of our knowledge, no study has explored the application of the ANFIS approach to estimate the software development effort on the basis of COCOMO datasets for training and testing This situation provided an impetus for the present study. The performance of the proposed ANFIS model is assessed and analyzed compared to the traditional methods With this investigation we have attempted to develop a better effort estimation tool, and to integrate soft computing techniques to software engineering.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
