Integrate the GM(1,1) and Verhulst Models to Predict Software Stage Effort
Integrate the GM(1,1) and Verhulst Models to Predict Software Stage Effort
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DOI：
10.1109/tsmcc.2009.2020690
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发表时间：
2009-11
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影响因子：
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通讯作者：
Yong Wang;Qinbao Song;Stephen G. MacDonell;M. Shepperd;Junyi Shen
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文献类型：
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作者：
Yong Wang;Qinbao Song;Stephen G. MacDonell;M. Shepperd;Junyi Shen
软件工作量预测在软件项目管理中起着至关重要的作用。为了与软件开发的动态方法保持一致，仅仅在早期阶段预测整个项目的工作量是不够的。相反，项目经理还必须动态地预测软件开发过程中不同阶段或活动的工作量。这可以帮助项目经理重新估计工作量并调整项目计划，从而避免工作量或进度超支。提出了一种基于灰色GM（1，1）模型和Verhulst模型的软件物理时间阶段-工作量预测方法。该方法根据特定类型的阶段努力序列动态地建立模型，并通过使用一种新的灰色反馈机制自动适应特定的开发方法。我们用一个大规模的真实世界的软件工程数据集来评估所提出的方法，并将其与线性回归方法和卡尔曼滤波方法进行比较，发现准确性分别提高了至少28%和50%。结果表明，该方法是有效的，具有相当大的潜力。我们相信，阶段预测可能是一个有用的补充，整个项目的努力预测方法。
Software effort prediction clearly plays a crucial role in software project management. In keeping with more dynamic approaches to software development, it is not sufficient to only predict the whole-project effort at an early stage. Rather, the project manager must also dynamically predict the effort of different stages or activities during the software development process. This can assist the project manager to reestimate effort and adjust the project plan, thus avoiding effort or schedule overruns. This paper presents a method for software physical time stage-effort prediction based on grey models GM(1,1) and Verhulst. This method establishes models dynamically according to particular types of stage-effort sequences, and can adapt to particular development methodologies automatically by using a novel grey feedback mechanism. We evaluate the proposed method with a large-scale real-world software engineering dataset, and compare it with the linear regression method and the Kalman filter method, revealing that accuracy has been improved by at least 28% and 50%, respectively. The results indicate that the method can be effective and has considerable potential. We believe that stage predictions could be a useful complement to whole-project effort prediction methods.