This issue includes four papers, covering performance mutation testing, performance regression localization, fault detection and localization, and defect prediction, respectively.

The first paper, by Pedro Delgado-Pérez, Ana Belén Sánchez, Sergio Segura and Inmaculada Medina-Bulo, concerns feasibility of applying performance mutation testing (i.e. applying mutation testing to assess performance tests) at the source-code level in general-purpose languages. To successfully apply performance mutation testing, the authors find it necessary to design specific mutation operators and mechanisms to evaluate the outputs. The authors define and evaluate seven new performance mutation operators to model known bug-inducing patterns. The authors report the results of experimental evaluation on open-source C++ programs. (Recommended by Professor Hyunsook Do)

The second paper, by Frolin S. Ocariza Jr. and Boyang Zhao, considers the problem of finding the causes of performance regression in software. Here, a performance regression is an increase in response time as a result of changes to the software. The paper describes a design, called ZAM, that automates the process of comparing execution timelines collected from web applications. Such timelines are used as the basis for finding the causes of performance regression. A number of challenges are introduced by the context in which, for example, timing information is typically noisy. The authors report the results of experimental evaluation and also experience in using the approach. (Recommended by Professor T. H. Tse)

The third paper, by Rawad Abou Assi, Wes Masri and Chadi Trad, concerns coincidental correctness and its impact on fault detection and localization. The authors consider weak coincidental correctness, in which a faulty statement is executed but this does not lead to an infected state. They also consider strong coincidental correctness, in which the execution of a faulty statement leads to an infected state but does not lead to incorrect output. The authors empirically investigated the effect of coincidental correctness on three classes of technique: spectrum-based fault localization (SBFL), test suite reduction (TSR) and test case prioritization (TCP). Interestingly, there was significant variation with, for example, evidence that coincidental correctness has a greater impact on TSR and TCP than on SBFL. (Recommended by Professor Hyunsook Do)

The fourth paper, by Zeinab Eivazpour and Mohammad Reza Keyvanpour, concerns the cost issue when handling the class imbalance problem over the training dataset in software defect prediction. The authors propose the cost-sensitive stacked generalization (CSSG) approach. This approach combines the staking ensemble learning method with cost-sensitive learning, which aims to reduce misclassification costs. In the CSSG approach, the logistic regression classifier and extra randomized trees ensemble method in cost-sensitive learning and cost-insensitive conditions are employed as a final classifier of stacking scheme. The authors report the results of experimental evaluation. (Recommended by Professor Hyunsook Do)

本期共收录四篇论文，分别涵盖性能变异测试、性能回归定位、故障检测与定位、缺陷预测。

第一篇论文由 Pedro Delgado-Pérez、Ana Belén Sánchez、Sergio Segura 和 Inmaculada Medina-Bulo 撰写，关注在通用语言的源代码级别应用性能变异测试（即应用变异测试来评估性能测试）的可行性. 为了成功应用性能变异测试，作者发现有必要设计特定的变异算子和机制来评估输出。作者定义并评估了七个新的性能变异算子，以模拟已知的错误诱导模式。作者报告了对开源 C++ 程序的实验评估结果。（道贤淑教授推荐）

由 Frolin S. Ocariza Jr. 和 Boyang Zhao 撰写的第二篇论文考虑了寻找软件性能回归原因的问题。在这里，性能回归是由于软件更改而导致响应时间增加。该论文描述了一种称为 ZAM 的设计，该设计可自动比较从 Web 应用程序收集的执行时间线的过程。此类时间表用作查找性能退化原因的基础。上下文引入了许多挑战，例如，时序信息通常是嘈杂的。作者报告了实验评估的结果以及使用该方法的经验。(谢天华教授推荐)

第三篇论文，由 Rawad Abou Assi、Wes Masri 和 Chadi Trad 撰写，关注巧合正确性及其对故障检测和定位的影响。作者考虑了弱巧合正确性，其中执行了错误的语句，但这不会导致受感染状态。他们还考虑了强巧合正确性，其中错误语句的执行会导致受感染状态，但不会导致错误输出。作者凭经验研究了巧合正确性对三类技术的影响：基于频谱的故障定位 (SBFL)、测试套件缩减 (TSR) 和测试用例优先级 (TCP)。有趣的是，存在显着差异，例如，巧合正确性对 TSR 和 TCP 的影响大于对 SBFL 的影响。（道贤淑教授推荐）

Zeinab Eivazpour 和 Mohammad Reza Keyvanpour 的第四篇论文涉及在软件缺陷预测中处理训练数据集上的类不平衡问题时的成本问题。作者提出了成本敏感的堆叠泛化 (CSSG) 方法。这种方法将 staking 集成学习方法与成本敏感学习相结合，旨在降低误分类成本。在 CSSG 方法中，成本敏感学习和成本不敏感条件下的逻辑回归分类器和额外随机树集成方法被用作堆叠方案的最终分类器。作者报告了实验评估的结果。（道贤淑教授推荐）