Resumen
Web Effort Estimation is an important estimation measure for predicting the effort required to develop a web application. The completion of web projects within stipulated time and budget is not possible without accurate effort estimation. The numerous effort estimation models are present these days and they have achieved a pinnacle of success, but the uncertainty features are daunting its progress due to deviations in the data set collected, types of projects, and data set characteristics. The literature studied for this research task elaborated that this field still lacks in a significant direction for consolidated documentation, which guides the researchers to choose a specific technique in order to predict the effort required for web application development. The wide and versatile nature of this domain daunting the researchers to mine the literature in a more appropriate way and deploy ensemble techniques of effort prediction models in order to achieve better results for web application viz., schedule delays, budget overruns. The systematic literature review (SLR) in this research task has been done to inspect the various aspects affecting the prediction accuracy of web applications and these identified characteristics lead to a better effort estimation model. The literature review is conducted on a collection of 143 papers retrieved from online journals and conference proceedings. Only 53 relevant papers are selected for broad investigation. The study reveals that the expert judgment and algorithm-based models are very popular and used frequently for effort prediction, instead the machine learning (ML) based models are rare in use but cater comparatively better prediction accuracy. The authors suggest taking cognizance of this research domain for developing ensembles of early effort prediction models to overcome delays in schedule and budget.