REU Site: Software Testing and Analytics

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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.