• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Advances in software engineering >Prediction Model for Object Oriented Software Development Effort Estimation Using One Hidden Layer Feed Forward Neural Network with Genetic Algorithm
【24h】

Prediction Model for Object Oriented Software Development Effort Estimation Using One Hidden Layer Feed Forward Neural Network with Genetic Algorithm

机译：基于遗传算法的一层隐层前馈神经网络的面向对象软件开发工作量预测模型

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The budget computation for software development is affected by the prediction of software development effort and schedule. Software development effort and schedule can be predicted precisely on the basis of past software project data sets. In this paper, a model for object-oriented software development effort estimation using one hidden layer feed forward neural network (OHFNN) has been developed. The model has been further optimized with the help of genetic algorithm by taking weight vector obtained from OHFNN as initial population for the genetic algorithm. Convergence has been obtained by minimizing the sum of squared errors of each input vector and optimal weight vector has been determined to predict the software development effort. The model has been empirically validated on the PROMISE software engineering repository dataset. Performance of the model is more accurate than the well-established constructive cost model (COCOMO).
机译：软件开发的预算计算受软件开发工作量和进度表的预测影响。可以根据过去的软件项目数据集准确地预测软件开发的工作量和进度。本文提出了一种使用一个隐层前馈神经网络（OHFNN）进行面向对象的软件开发工作量估算的模型。通过将从OHFNN获得的权重向量作为遗传算法的初始种群，进一步借助遗传算法对该模型进行了优化。通过最小化每个输入向量的平方误差总和获得了收敛性，并且确定了最佳权重向量以预测软件开发工作。该模型已在PROMISE软件工程存储库数据集中进行了经验验证。该模型的性能比公认的建设性成本模型（COCOMO）更准确。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号