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Applying Genetic Programming for Estimating Software Development Effort of Short-scale Projects

机译:应用遗传编程估算小规模项目的软件开发量

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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 projects developed by 40 programmers was used for generating the three models, whereas another sample of 77 projects developed by 24 programmers was used for validating those three models. Accuracy results from the model obtained with genetic programming suggest that it could be used to estimate software development effort of short-scale projects when these projects are developed in a disciplined manner within a development controlled environment.
机译:统计回归和神经网络经常被用来估计大型和小型软件项目的开发成果。在本文中,使用了一种遗传程序设计技术,目的是估计在开发小规模项目中所需的工作量。将获得的结果与使用前两种技术生成的结果进行比较。由40个程序员开发的132个小规模项目的样本用于生成这三个模型,而由24个程序员开发的77个项目的另一个样本用于验证这三个模型。通过遗传编程获得的模型的准确性结果表明,当在开发控制的环境中以纪律方式开发这些项目时,可以将其用于估算小规模项目的软件开发工作量。

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