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A severe problem that impacts the software project is inaccurate estimation of the effort. Estimation of the software development effort remains an intricate problem. The complexity of the software and its scope are increasing alarmingly which attracts many researchers. Past the decades numerous techniques have been introduced and implemented. Many of them have given good results with acceptable error...
In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable...
Statistical regression and neural networks have frequently been used to estimate the development effort of both short and large software projects. In this paper, a genetic programming technique is used with the goal of estimating the effort required in the development of short-scale projects. Results obtained are compared with those generated using the first two techniques. A sample of 132 short-scale...
This paper discusses nearest-neighborhood linear regression methods in a statistical view of learning and present an application of these models to software project effort estimation. The usefulness of the models is highlighted through experiments with a well-known NASA software project data set. A comparative study with global regression methods such as bagging predictors, support vector regression,...
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