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The effort estimation needs to be done at early stages for successful delivery of software. Numerous models have been developed to estimate software effort during the last decades, but effort estimation of a software project is still a challenging task and in the case of web based projects, it is even harder. The selection of programming language and use of different type of objects i.e. hyperlinks, graphics, and scripts etc. make the web effort estimation process really complex. An estimation model "WebMo", proposed to estimate the effort of web based projects inspired by COCOMO. This research presents a non-algorithmic model named "Neuro-Web" based on Artificial Neural Networks (ANN). The proposed model will use the WebMo parameters as input. These parameters include web application size, productivity coefficients, and 9 different cost drivers. This proposed model is calibrated using the dataset of 164 real-life web applications developed by different freelancers and software houses. The "Neuro-Web" model is compared with the existing model "WebMo" and results reveal that Neuro-Web performs better than "WebMo". The MMRE of the proposed method is just 9.92% as compared to 26.27% for WebMo.
Qamar et al. (Fri,) studied this question.