基于岭回归和进化的软件工作量估计 属性选择

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摘要翻译：
软件成本估算是软件开发启动阶段的前提管理活动之一，在整个软件生命周期中反复进行，从而对总成本进行修正。在软件成本估计中，通常使用项目属性的选择来产生交付软件产品的预期人力资源的努力估计。然而，在每种情况下选择合适的项目成本动因需要项目经理代表大量的经验和知识，而这些经验和知识只能通过多年的软件工程实践来获得。许多研究表明，文献中用于软件成本估计的流行方法，如线性回归，不够稳健，不能产生准确的预测。近年来，双变量岭回归(RR)技术被用于努力估计，取得了很好的结果。在这项工作中，我们表明，如果使用人工智能方法来自动选择适当的项目成本动因（输入），结果可能会进一步改进。我们提出了一种结合RR和遗传算法的混合方法，后者通过进化属性子集来更准确地逼近努力。将该混合代价模型应用于一个广为人知的高维软件项目样本数据集，结果表明，剔除冗余属性可以提高模型的精度。
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英文标题：
《Software Effort Estimation with Ridge Regression and Evolutionary
  Attribute Selection》
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作者：
Efi Papatheocharous, Harris Papadopoulos and Andreas S. Andreou
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Software Engineering        软件工程
分类描述：Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
涵盖设计工具、软件度量、测试和调试、编程环境等。大致包括ACM所有主题课程D.2的材料，除了D.2.4（程序验证）可能应该有计算机科学中的逻辑作为主要主题领域。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Software cost estimation is one of the prerequisite managerial activities carried out at the software development initiation stages and also repeated throughout the whole software life-cycle so that amendments to the total cost are made. In software cost estimation typically, a selection of project attributes is employed to produce effort estimations of the expected human resources to deliver a software product. However, choosing the appropriate project cost drivers in each case requires a lot of experience and knowledge on behalf of the project manager which can only be obtained through years of software engineering practice. A number of studies indicate that popular methods applied in the literature for software cost estimation, such as linear regression, are not robust enough and do not yield accurate predictions. Recently the dual variables Ridge Regression (RR) technique has been used for effort estimation yielding promising results. In this work we show that results may be further improved if an AI method is used to automatically select appropriate project cost drivers (inputs) for the technique. We propose a hybrid approach combining RR with a Genetic Algorithm, the latter evolving the subset of attributes for approximating effort more accurately. The proposed hybrid cost model has been applied on a widely known high-dimensional dataset of software project samples and the results obtained show that accuracy may be increased if redundant attributes are eliminated.
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PDF链接：
https://arxiv.org/pdf/1012.5754

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关键词：工作量 性选择 岭回归 Presentation Intelligence 进行 Regression estimation Ridge 回归