A random forest model for early-stage software effort estimation for the SEERA dataset
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Investigating the use of random forest in software effort estimation
AbstractOver the last two decades, there has been an important increase in studies dealing with the software development effort estimation (SDEE) using machine learning (ML) techniques that aimed to improve the accuracy of the estimates and to understand ...
SEERA: a software cost estimation dataset for constrained environments
PROMISE 2020: Proceedings of the 16th ACM International Conference on Predictive Models and Data Analytics in Software EngineeringThe accuracy of software cost estimation depends on the relevancy of the cost estimation dataset, the quality of its data and its suitability for the targeted software development environment. Software development cost is impacted by technical, socio-...
Early stage software effort estimation using random forest technique based on use case points
Due to the increasing complexity of software development activities, the need for effective effort estimation techniques has arisen. Underestimation leads to disruption in the project's estimated cost and delivery. On the other hand, overestimation causes ...
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