Special Issue: IEEE International Conference on Software Testing, Validation & Verification 2018

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software Testing Verification & Reliability Pub Date : 2021-01-01 DOI:10.1002/stvr.1764
R. Feldt, S. Yoo
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Abstract

This special issue contains extended versions of two papers from the 11th IEEE International Conference on Software Testing, Validation & Verification (ICST 2018). ICST strives to provide an open forum for researchers, scientists, engineers, and practitioners working on software testing to present and discuss the latest research findings, ideas and developments. This was no exception at ICST 2018, where the collocation with the quarterly meeting of Swedish Association of Software Testing (SAST) led to strong industry presence and cross pollination of ideas. Based on the reviews from the program committee members, as well as the discussion with the Editors-in-Chief about their relevance to STVR, we invited authors of three papers to extend their papers and submit to the special section. The submitted manuscript went through a rigorous reviewing process by a panel of experts that included, but was not limited to, the members of the ICST 2018 program committee. During the process, two of the three papers were handled by one of us (Prof. Feldt) due to conflicts of interest. After finalizing the review and revision process two papers were accepted for publication in this special issue. The first paper, “DeMiner: Test Generation for High Test Coverage through Mutant Exploration” by Shin Hong, Yunho Kim, and Moonzoo Kim, introduces a way of using mutation analysis to improve the code coverage achieved by concolic testing. Called “Invasive Software Testing”, the proposed technique exploits additional information about the concrete and dynamic behaviour of the System Under Test, via mutation testing, to set goalposts for improved concolic testing. The second paper, “Effective Repair of Internationalization Presentation Failures in Web Applications Using Style Similarity Clustering and Search-Based Techniques” by Sonal Mahajan, Abdulmajeed Alameer, Phil McMinn, and William Halfond, introduces an automated repair technique for presentation failures in web pages regarding internationalization. The proposed technique, IFix+, can address presentation failures that stem from interactions of multiple style components by clustering DOM elements based on their visual appearances. Subsequently, IFix+ uses a search based approach to find a CSS patch that avoids introduction of any new failures. We express our thanks to everyone who contributed to the success of ICST 2018, as well as to this special section. We are grateful to the authors for extending their paper and submitting their valuable work to STVR. We would also like to express our gratitude to everyone who worked hard to make ICST 2018 the great success it was: in the midst of a global pandemic, the memory of the conference is something we all cherish deeply. In particular, we thank the general chair, Hans Hansson. Finally, we would like to thank both Robert Hierons and Tao Xie, for their support and guidance for this special issue.
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特刊:2018年IEEE软件测试、验证与验证国际会议
本特刊包含来自第11届IEEE软件测试,验证与验证国际会议(ICST 2018)的两篇论文的扩展版本。ICST致力于为从事软件测试的研究人员、科学家、工程师和实践者提供一个开放的论坛,以展示和讨论最新的研究成果、想法和发展。这在ICST 2018上也不例外,与瑞典软件测试协会(SAST)季度会议的搭配导致了强大的行业存在和思想的交叉授粉。根据项目委员会成员的意见,以及与总编辑讨论了他们与STVR的相关性,我们邀请了三篇论文的作者扩展他们的论文并提交到专区。提交的手稿经过了包括但不限于ICST 2018项目委员会成员在内的专家小组的严格审查。在此过程中,由于利益冲突,我们其中一人(Feldt教授)处理了三篇论文中的两篇。在完成审查和修订过程后,两篇论文被接受在本期特刊上发表。第一篇论文,由Shin Hong, Yunho Kim和Moonzoo Kim撰写的“DeMiner:通过突变探索实现高测试覆盖率的测试生成”,介绍了一种使用突变分析来提高通过聚合测试实现的代码覆盖率的方法。这种被称为“侵入式软件测试”的技术,通过突变测试,利用关于被测系统的具体和动态行为的额外信息,为改进的整体测试设定目标。第二篇论文“使用风格相似聚类和基于搜索的技术有效修复Web应用程序中的国际化表示失败”,作者是Sonal Mahajan、Abdulmajeed Alameer、Phil McMinn和William Halfond,介绍了一种针对国际化页面中表示失败的自动修复技术。所提出的技术IFix+可以通过基于视觉外观对DOM元素进行聚类来解决由于多个样式组件的交互而导致的表示失败。随后,IFix+使用基于搜索的方法来查找CSS补丁,从而避免引入任何新的故障。我们感谢所有为ICST 2018的成功举办做出贡献的人,以及这个特别版块。我们非常感谢作者将他们的论文延伸到STVR并提交他们的宝贵工作。我们还要感谢为2018年ICST取得巨大成功而努力工作的每一个人:在全球大流行的背景下,对会议的记忆是我们都深深珍惜的。我们特别感谢总主席汉斯·汉森。最后,我们要感谢Robert Hierons和Tao Xie对本期特刊的支持和指导。
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来源期刊
Software Testing Verification & Reliability
Software Testing Verification & Reliability 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
34
审稿时长
>12 weeks
期刊介绍: The journal is the premier outlet for research results on the subjects of testing, verification and reliability. Readers will find useful research on issues pertaining to building better software and evaluating it. The journal is unique in its emphasis on theoretical foundations and applications to real-world software development. The balance of theory, empirical work, and practical applications provide readers with better techniques for testing, verifying and improving the reliability of software. The journal targets researchers, practitioners, educators and students that have a vested interest in results generated by high-quality testing, verification and reliability modeling and evaluation of software. Topics of special interest include, but are not limited to: -New criteria for software testing and verification -Application of existing software testing and verification techniques to new types of software, including web applications, web services, embedded software, aspect-oriented software, and software architectures -Model based testing -Formal verification techniques such as model-checking -Comparison of testing and verification techniques -Measurement of and metrics for testing, verification and reliability -Industrial experience with cutting edge techniques -Descriptions and evaluations of commercial and open-source software testing tools -Reliability modeling, measurement and application -Testing and verification of software security -Automated test data generation -Process issues and methods -Non-functional testing
期刊最新文献
Model‐based testing, test case prioritization and testing of virtual reality applications In vivo testing and integration of proving and testing Mutation testing optimisations using the Clang front‐end Semantic‐aware two‐phase test case prioritization for continuous integration Exploiting deep reinforcement learning and metamorphic testing to automatically test virtual reality applications
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