CAREER: Enhancing Software Testing by Generating, Suggesting, and Maintaining Carved Tests

Software quality is critical to the success of software systems, and effective testing practices are key to achieving high quality. Yet, many organizations continue to struggle to release high-quality software because they often rely heavily on large end-to-end tests that validate complete user workflows, making it difficult and costly to verify frequent code changes effectively. The project’s novelties are approaches that generate small, focused tests from larger existing tests, recommend the most relevant of these generated tests in response to code changes, and keep them aligned as software evolves. The project’s broader significance and importance are that it helps organizations use existing testing resources more effectively, supports the development of higher-quality software, and advances education in software testing. The project ultimately prepares students and practitioners to build and maintain stronger test suites, with the long-term benefit of more dependable software. This project is centered on three research goals. First, it develops automated techniques for generating valid, actionable, and understandable carved tests, which are focused tests extracted from larger tests. Second, it develops automated and AI-based techniques for suggesting carved tests that are relevant to specific code changes, enabling focused and effective regression testing. Third, it develops automated techniques for maintaining alignment between carved tests and the evolving original tests so that extracted tests remain useful over time. These goals are pursued through a combination of dynamic analysis, static analysis, code-change analysis, and traceability analysis, and the resulting techniques are integrated into an open-source framework. The project also has an educational goal to incorporate test repurposing concepts into software engineering curricula through instructional modules and projects for students, while also supporting the continuing education of software professionals. The project advances the foundations and practice of software testing and helps prepare a workforce skilled in producing high-quality software. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2541053 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT,01003031DB NSF RESEARCH & RELATED ACTIVIT,01002930DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Mattia Fazzini | Institution: University of Minnesota-Twin Cities, MINNEAPOLIS, MN | Award Amount: $405,207 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541053 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541053.html