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首页> 外文会议>2010 IEEE/ACS International Conference on Computer Systems and Applications >Software effort estimation by tuning COOCMO model parameters using differential evolution
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Software effort estimation by tuning COOCMO model parameters using differential evolution

机译：通过使用差分演化调整COOCMO模型参数来估算软件工作量

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摘要

Accurate estimation of software projects costs represents a challenge for many government organizations such as the Department of Defenses (DOD) and NASA. Statistical models considerably used to assist in such a computation. There is still an urgent need on finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimize such a relationship via tuning mathematical model parameters is urgently needed. In [1] two new model structures to estimate the effort required for software projects using Genetic Algorithms (GAs) were proposed as a modification to the famous Constructive Cost Model (COCOMO). In this paper, we follow up on our previous work and present Differential Evolution (DE) as an alternative technique to estimate the COCOMO model parameters. The performance of the developed models were tested on NASA software project dataset provided in [2]. The developed COCOMO-DE model was able to provide good estimation capabilities.
机译：准确估算软件项目成本对许多政府组织（如国防部（DOD）和NASA）构成了挑战。大量用于辅助这种计算的统计模型。仍然迫切需要找到一种数学模型，该模型可以在软件项目的工作量/成本与成本动因之间提供准确的关系。迫切需要一种能够通过调整数学模型参数来优化这种关系的强大算法。在[1]中，提出了两个新的模型结构来估计使用遗传算法（GA）进行软件项目所需的工作量，作为对著名的建设性成本模型（COCOMO）的修改。在本文中，我们跟进了先前的工作，并提出了差分演化（DE）作为估计COCOMO模型参数的替代技术。在[2]中提供的NASA软件项目数据集上测试了开发模型的性能。开发的COCOMO-DE模型能够提供良好的估算能力。

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