Pareto有效的多目标优化局部调整类比估计
Pareto Efficient Multi Objective Optimization for Local Tuning of Analogy Based Estimation
摘要：
基于类比的工作量估算（ABE）是软件工作量估算的重要方法之一. ABE的基本概念更接近于专家评估的心态，但具有一个自动化的过程，其中通过重用类似的历史项目来生成最终评估。使用ABE时的主要关键问题是如何适应检索到的最近邻的努力。自适应过程是ABE的一个重要组成部分，可以使用一些自适应策略，基于调整所选择的原始解决方案来生成更成功的准确估计。在这项研究中，我们发现有三个相互关联的决策变量对自适应方法的成功有很大的影响：（1）最近的类比数（k），（2）适应所需的最佳特征集，（3）适应权重。为了找到关于这些变量的正确决策，需要研究所有可能的组合并单独评估它们，以选择可以改善所有预测评估措施的组合。现有的评价措施通常表现不同，有时在评价预测方法时呈现相反的趋势。这意味着改变一个决策变量可以改善一个评价指标，同时降低其他指标。因此，本研究的主题是如何找出最佳的决策变量，以改善适应策略，从而提高整体的评价措施，而不降低其他。这些决定的影响还没有被调查之前，因此，我们建议把建设的适应过程作为一个多目标优化问题。在优化多个评价指标的基础上，利用粒子群优化算法（PSO）对这些决策变量进行寻优
英文摘要：
Analogy Based Effort Estimation (ABE) is one of the prominent methods for software effort estimation. The fundamental concept of ABE is closer to the mentality of expert estimation but with an automated procedure in which the final estimate is generated by reusing similar historical projects. The main key issue when using ABE is how to adapt the effort of the retrieved nearest neighbors. The adaptation process is an essential part of ABE to generate more successful accurate estimation based on tuning the selected raw solutions, using some adaptation strategy. In this study we show that there are three interrelated decision variables that have great impact on the success of adaptation method: (1) number of nearest analogies (k), (2) optimum feature set needed for adaptation, and (3) adaptation weights. To find the right decision regarding these variables, one need to study all possible combinations and evaluate them individually to select the one that can improve all prediction evaluation measures. The existing evaluation measures usually behave differently, presenting sometimes opposite trends in evaluating prediction methods. This means that changing one decision variable could improve one evaluation measure while it is decreasing the others. Therefore, the main theme of this research is how to come up with best decision variables that improve adaptation strategy and thus, the overall evaluation measures without degrading the others. The impact of these decisions together has not been investigated before, therefore we propose to view the building of adaptation procedure as a multi-objective optimization problem. The Particle Swarm Optimization Algorithm (PSO) is utilized to find the optimum solutions for such decision variables based on optimizing multiple evaluation measures
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