The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR-SMO-PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR-SMO-PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε-SVR and ʋ-SVR in the seven data sets. Thus, the SVR-SMO-PUK is useful to software managers to predict CE.

中文翻译：

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软件项目建设阶段的工作量预测

需要构建阶段工作量预测，以将资源分配给专门用于软件开发生命周期 (SDLC) 阶段的从业人员团队。据报道，施工工作量 (CE) 占整个 SDLC 工作量的 27.5% 至 58%，导致将这些百分比作为参考的不确定性。支持向量回归 (SVR) 训练涉及二次规划问题，可以使用顺序最小优化 (SMO) 算法分析解决这些问题。此外，Pearson VII (PUK) 内核可用于替换 SVR 常用的一组内核函数。本研究的目的是应用 SMO 和 PUK 来训练 SVR 以预测 CE。用 SMO 算法训练的 SVR 模型以 PUK (SVR-SMO-PUK) 预测精度为内核，与从统计回归 (SR)、神经网络 (NN) 和两种类型的 SVR 获得的精度进行统计比较。使用了七个国际公共软件项目数据集。结果表明，SVR-SMO-PUK 在五个数据集中优于 SR，在这五个数据集中的两个数据集中优于 NN。在其余两个数据集中，它等于 SR 和 NN。它等于 在其余两个数据集中，它等于 SR 和 NN。它等于 在其余两个数据集中，它等于 SR 和 NN。它等于ε- SVR 和ʋ- SVR 在七个数据集中。因此，SVR-SMO-PUK 对软件经理预测 CE 很有用。