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Early stage software effort estimation using random forest technique based on use case points

机译：基于用例点的使用随机森林技术的早期软件工作量估计

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摘要

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 outbidding and financial losses in business. Effective software effort estimation techniques enable project managers to schedule the software life cycle activities appropriately. Correctly assessing the effort needed to develop a software product is a major concern in software industries. Random forest (RF) technique is a popularly used machine learning technique that helps in improving the prediction values. The main objective of this study is to precisely assess the software projects development effort by utilising the use case point approach. The effort parameters are optimised utilising the RF technique to acquire higher prediction accuracy. Moreover, the results acquired applying the RF technique is compared with the multi-layer perceptron, radial basis function network, stochastic gradient boosting and log-linear regression techniques to highlight the performance attained by each technique.
机译：由于软件开发活动的复杂性不断提高，因此需要有效的工作量估算技术。估计不足会导致项目的估计成本和交付中断。另一方面，高估会导致竞标和业务损失。有效的软件工作量估算技术使项目经理可以适当地安排软件生命周期活动。正确评估开发软件产品所需的工作是软件行业的主要关注点。随机森林（RF）技术是一种广泛使用的机器学习技术，可帮助改善预测值。这项研究的主要目的是通过使用用例点方法来精确评估软件项目的开发工作。利用RF技术优化工作量参数以获得更高的预测精度。此外，将使用RF技术获得的结果与多层感知器，径向基函数网络，随机梯度增强和对数线性回归技术进行比较，以突出每种技术所获得的性能。

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