A hybrid model of wavelet neural network and metaheuristic algorithm for software development effort estimation

Abstract

Software development effort estimation (SDEE) means estimating software cost and effort at the early stage of software development. It is a difficult task as the characteristics of software to be developed are not known at the time of estimation of software effort. But this is very important for software development organizations as it ultimately leads to the project’s success. This paper proposes a non-algorithmic technique for SDEE i.e. a hybrid model of wavelet neural network (WNN) and metaheuristic algorithm. Two metaheuristic algorithms i.e. firefly algorithm and bat algorithm are used. The efficiency of WNN with integration of each of these metaheuristic algorithms is investigated. Two variants of wavelet functions—Morlet and Gaussian are used as activation functions in WNN. The proposed techniques are experimentally evaluated on PROMISE SDEE repositories. It was observed that integrating metaheuristic algorithms with WNN outperformed the results of software effort prediction in comparison to traditional WNN which is not optimized using any metaheuristic technique. The results are also statistically validated using a non-parametric statistical test using IBM SPSS tool.