REU Site: Software Testing and Analytics

该项目将在东卡罗莱纳大学（ECU）建立一个为期三年的软件测试和分析REU站点。它将在夏季学期为10名本科生提供为期10周的研究项目。师生互动以及学生之间的互动将采取不同的形式，如会议、研讨会、辅导课、工作坊和实地考察。REU项目将允许不同的本科生体验尖端的研究经验，这将帮助他们在STEM研究中自力更生。学生将获得宝贵的研究技能，这将为他们未来的研究领域做准备，他们对研究的接触将帮助他们在创新的就业市场中竞争高科技领域。研究经历也将激励他们继续研究生学习。REU项目还将在夏季项目结束后为学生提供与全国各地的教师导师和学生同行合作的机会。样本研究项目涵盖了软件测试和分析中的开放研究主题。科学软件的软件测试与分析是研究如何对复杂的科学软件系统进行充分测试的技术。测试产生的实验数据将使用机器学习工具进行分析，以提高测试效率和有效性。我们希望学生能够掌握软件测试的基本原理，并能够熟练地创建测试策略和使用测试科学软件的工具。组合测试用例的故障检测有效性和MC/DC覆盖率将研究组合测试和MC/DC（修改条件/决策覆盖率）测试的集成。如何有效地测试逻辑表达式，对输入域的不同分区和可能影响基于组合的测试生成的因素的敏感性分析，以及使用不同组合测试算法生成的测试之间的交叉比较等研究将进行。学生将在这个项目中接受严格的软件测试和软件测试研究方面的训练。《Mobile Domain Specific Language (DSL) Construction的软件分析》将分析程序分析结果，以改进DSL的开发；《Guided Test Generation for Web Applications》将使用程序分析结果派生测试，用于测试Web应用程序。这两个项目将为学生提供学习程序分析原理、应用和实验研究的机会。

This project will establish a three-year REU site in software testing and analytics at East Carolina University (ECU). It will offer a ten-week research program for ten undergraduate students during summer semesters. The faculty-student interaction as well as interaction among students will take different forms such as meetings, seminars, tutorials, workshop, and field trips. The REU project will allow a diverse pool of undergraduate students to experience cutting-edge research experience that will help them to become self reliant in STEM research. Students will gain valuable research skills that will prepare them for their future fields of study, and their exposure to the research will help them to compete for high technology fields in an innovative job market. The research experience will also motivate them to continue onto graduate studies. The REU project also will provide students an opportunity to collaborate with their faculty mentors and student peers across the nation after the summer program. The sample research projects cover open research topics in software testing and analytics. Software Testing and Analysis of Scientific Software is to investigate the technique for adequately testing complex scientific software systems. The experimental data generated from the testing will be analyzed with machine learning tools for improving the test efficiency and effectiveness. We expect students will master basic principles of software testing and become skillful in creating test strategies and using tools for testing scientific software. Fault Detection Effectiveness and MC/DC Coverage of Combinatorial Test Cases will investigate the integration of combinatorial testing and MC/DC (modified condition/decision coverage) testing. Studies such as how logical expressions can be effectively tested, sensitivity analysis of different partitions of the input domain and factors that may affect combinatorial-based test generation, and a cross comparison between tests generated using different combinatorial testing algorithms will be conducted. Students will receive rigorous training in software testing and software testing research in this project. Software Analytics for Mobile Domain Specific Language (DSL) Construction will analyze program analysis results for the improvement of the development of DSL, and Guided Test Generation for Web Applications will use program analysis results to derive tests for testing web applications. The two projects will offer students the opportunity to learn the principles, applications and experimental study of program analysis.