An Optimized Neuro-Fuzzy Network for Software Project Effort Estimation

AbstractEstimating software effort is an essential project management practice. The software effort estimation is the process of predicting the time and efforts required in the development of software projects. Estimating software cost is a critical challenge during the planning stage. Due to the lack of availability of quality data and the dynamic nature of software development, accurate estimation is difficult. This paper proposes an efficient software cost estimation method for the Genetic Elephant Herding Optimization-based Neuro-Fuzzy Network (GEHO-based NFN). In this method, a neuro-fuzzy network (NFN) is used to predict the software efforts in terms of cost. The training of this NFN is optimized using the genetic elephant herding optimization (GEHO) method, which combines the features of the genetic algorithm (GA) and the elephant herding optimization (EHO) techniques. The performance of the developed method was evaluated using five historical & benchmark datasets from the industrial projects. These are based on the four widely used performance evaluation metrics, such as Mean magnitude of relative Error (MMRE), Median magnitude of relative error (MdMRE), Root Mean Square Error (RMSE), and Prediction Accuracy (PRED). The Comparative analysis and experimental results of the proposed method conclude that the performance of the GEHO-based NFN method is better than other popular soft computing methods like Linear regression, Support Vector Regression (SVR), Wavelet ANN (WNN), and Decision Tree (DT) based Software project effort estimation (SPEE) methods.KEYWORDS: Genetic elephant herding optimization (GEHO)Genetic algorithm (GA)Industrial projectsNeuro-fuzzy network (NFN)Mean magnitude of relative error (MMRE)Software project effort estimation (SPEE) ACKNOWLEDGEMENTSThe infrastructure & research facilities provided by Birla institute of Technology, Jaipur Campus, for the presented work is dully acknowledged.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSudhir SharmaSudhir Sharma received his B.E. & M.E. degree from BIT, Mesra, Ranchi. He is currently working as an Assistant Professor in the Department of Computer Science and Engineering at Birla Institute of Applied Sciences, Nainital, Uttarakhand, India. He is pursuing his Ph.D. degree in Computer Science & Engineering from BIT, Mesra, Jaipur campus, Rajasthan. His research interests include Soft Computing, Machine Learning, Software Project Management, Cryptography & Network Security, Automata Theory, and Mobile Computing. He has more than ten years of teaching & learning experience & has published more than 10 Scopus & SCI-indexed research papers in reputed National and International journals. Corresponding author. Email: Sudhir33.04@bitmesra.ac.inShripal VijayvargiyaDr. Shripal Vijayvargiya is an Associate Professor at the Department of CSE, BIT Mesra Jaipur campus, Rajasthan, India. He has received his Ph.D. degree from BIT, Mesra, Ranchi, Jharkhand. He has more than nineteen years of vast teaching & learning experience. His area of research includes Soft Computing, Machine Learning, and Data Mining. Email: svijayvargiya@bitmesra.ac.in