Systematic Literature Review on Effort Estimation by Software Development Life Cycle Phases

Abstract

Several techniques have been proposed to estimate the total effort of the Software Development Life Cycle (SDLC) rather than by SDLC phase to be performed for an independent team. An accurate effort estimation is needed for software managers to create realistic plans and allocate resources appropriately to independent teams. Therefore, this Systematic Literature Review (SLR), unlike other secondary studies, examines the current state of effort estimation by SDLC phase instead of estimating it across the entire SDLC. In addition, this SLR identifies metaheuristics used for optimizing the parameters of effort estimation models. We searched for studies whose objective has been to propose models for estimating the effort of specific SDLC phases, rather than total SDLC effort. We firstly identified 216 studies, and finally we selected 31 of them published between 2014 and march 2025 in journals and conferences. The majority of the studies investigated effort estimation in the testing and maintenance phases, mainly using Machine Learning (ML) techniques. Functional size and project characteristics were the most common explanatory variables. International Software Benchmarking Standards Group was the predominant dataset for training models and the mean of Absolute Residual was the recommended precision measure. Cross-validation was the most used model validation method. We can conclude that more research is needed on effort estimation by SDLC phases such as requirements specification, design, and construction, as well as to further explore ML and metaheuristics to improve the prediction accuracies of models.