软件工作量估计：岭回归与进化属性选择
Software Effort Estimation with Ridge Regression and Evolutionary Attribute Selection
AI总结：
针对软件成本估算中属性选择依赖经验且传统方法不准确的问题，本文提出结合岭回归与遗传算法的混合模型，自动选择成本驱动因素，并在高维数据集上验证了消除冗余属性可提高估算精度。
摘要：
软件成本估算是软件开发启动阶段开展的一项先决性管理活动，并在整个软件生命周期中反复进行，以便对总成本进行修正。在软件成本估算中，通常利用一组项目属性来估算交付软件产品所需的人力资源工作量。然而，在每种情况下选择合适的项目成本驱动因素，需要项目经理具备大量的经验和知识，而这些只能通过多年的软件工程实践获得。多项研究表明，文献中常用的软件成本估算方法（如线性回归）不够稳健，且无法提供准确的预测。近年来，对偶变量岭回归（RR）技术已被用于工作量估算，并取得了有前景的结果。在本工作中，我们证明如果使用人工智能方法自动为该技术选择合适的项目成本驱动因素（输入），结果可进一步改善。我们提出了一种将岭回归与遗传算法相结合的混合方法，后者通过进化属性子集来更准确地逼近工作量。所提出的混合成本模型已应用于一个广为人知的高维软件项目样本数据集，获得的结果表明，若消除冗余属性，可以提高准确性。
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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