A Generic Analogy-Centered Software Cost Estimation Based on Differential Evolution Exploration Process

Abstract

Software cost estimation is the prediction of development effort and calendar time required to develop any software project. It is considered to be the very fundamental task for successful execution of an on-going project as well as budgetary requirements of futuristic projects. As accuracy in software cost estimation is very hard because of the availability of vague information at the time of inception of the software project, it prompted many researchers to explore in this domain from past decades. Their pioneer works suggest a bulk of techniques for this purpose. However, because of the availability of large number of estimation techniques, it becomes hard for any software practitioners to select an appropriate one. To help the industry practitioners in these situations, a novel analogy-centered model based on differential evolution exploration process is proposed in this research study. The proposed model has been assessed on 676 projects from 5 different data sets and the results achieved are significantly better when compared with other benchmark analogy-based estimation studies. Furthermore, being the very less computational cost of the proposed model, it is suggested that the proposed model be considered as the preliminary stage of any analogy-based software estimation technique.

Software Development Analytics in Practice: A Systematic Literature Review

“…[S34] To help the industry practitioners in these situations, a analogycentered model based on differential evolution exploration process is proposed in this research study. The proposed model has been assessed on 676 projects from 5 different data sets and the results achieved are significantly better when compared with other benchmark analogy-based estimation studies [67].…”

Gradient Boosting Optimized Through Differential Evolution for Predicting the Testing Effort of Software Projects

“…The parameters used for each metaheuristic are presented in Table 9, where it can be observed that for DE, we encourage the diversity of vectors in the population with a value of F = 0.8 (suggested in [69]) and a value of CR =0.5 (suggested in [59] [70]) promoting a balance of crossover probability.…”