Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.

中文翻译：

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使用基于搜索的技术预测面向对象软件的软件缺陷

没有任何缺陷的发展是没有实质的。及时检测软件缺陷有利于正确利用资源，从而节省时间、精力和金钱。随着软件规模和复杂性的增加，对准确高效的预测模型的需求也在增加。最近，基于搜索的技术 (SBT) 吸引了许多软件缺陷预测 (SDP) 研究人员。本研究的目的是进行实证评估，以评估 SBT 在预测面向对象 (OO) 软件中的软件缺陷方面的适用性。在这项研究中，利用 16 个 SBT 为 13 个 OO 软件项目构建缺陷预测模型。稳定的性能测量——Gmean、Balance 和 Receiver Operating Characteristic-Area Under Curve (ROC-AUC) 用于探索开发模型的预测能力，考虑到软件数据集的不平衡性。采取了适当的措施来处理 SBT 的随机行为。使用符合 Wilcoxon 事后分析的弗里德曼检验统计验证结果的重要性。结果证实，借助 SBT，可以在软件开发的早期阶段检测到软件缺陷。本文确定了 SBT 的有效子集，它将帮助软件从业者及时检测可能的软件缺陷，从而节省资源并开发出高质量的软件。八个 SBT — 监督分类系统 (UCS)、面向生物信息学的分层进化学习 (BIOHEL)、CHC、基于遗传算法的自适应离散区间分类器系统 (GA_ADI)、