基于计算神经网络的软件项目时间预测估计

P. Vidya Sagar, C. Krishna, Nageswara Rao Moparthi
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引用次数: 2

摘要

编程风险管理(SPM)将在等级变量中脱颖而出,将编程的胜利交替失望。对产品改进机会的预测可能是SPM成功的关键任务。预测组件的正确性和不动摇的质量可能同样是必要的。在这个项目中,我们看到了独特的机器,采取适当的策略应该准确地预见编程机会。当记录风险数据时,跨度信息进一步其他任务信息将用于消耗或跨度预测,这一努力背后的基本灵感将是这些信息存储在记录的数据集中可以用来创建预测模型,最终汤姆的阅读可能的事实系统,例如,直接复发,通信调查或机器采取策略,例如,ANN(人工神经网络)又称SVM(支持向量机),与预测有关风险的施加交替跨度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of software project time prediction using Computational Neural Networks
Programming venture administration (SPM) will be a standout amongst the grade variables will program victory alternately disappointment. Prediction for product improvement chance may be the key errand for the successful SPM. Those correctness and unwavering quality of prediction components may be likewise imperative. In this project, we look at distinctive machine Taking in strategies in place should faultlessly foresee the programming chance. The point when chronicled venture exert data, span information Furthermore other task information would utilized for exertion or span prediction, the essential inspiration behind this endeavor will be that those information put away in the recorded datasets might be used to create predictive models, Eventually Tom’s perusing Possibly Factual systems for example, straight relapse Also correspondence Investigation or machine Taking in strategies for example, ANN (Artificial neural Network) Also SVM (Support vector Machine), with anticipate the exert alternately span about ventures.
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