Editorial: Testing, Debugging, and Defect Prediction

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)