I-Corps: Translation Potential of Optimizing Regression Testing in Software Development

University Of Texas At Austin, Austin TX

Investigators

Abstract

The broader impact of this I-Corps project is the development of technology that will enable companies to substantially reduce the number of software tests they have to run for any code change, thus reducing their software testing cost and improving the developer’s productivity. This technology has the potential to reduce testing time by over 50% on average, across a large number of code changes. The integration of the technology into already existing workflows will introduce minimal disruptions and remain hidden from most software developers, increasing the chance for adoption. This research will bring: (a) a reduction in testing time and faster feedback to developers, (b) a reduction in required resources to run tests which will reduce overall cost and maintenance cost, and (c) an alignment with green computing efforts and carbon neutrality. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a regression test selection tool (RTS) that reduces testing cost by skipping tests that are not impacted by code changes. This technology has the potential to be integrated in most common development workflows. The tool can capture dependencies for each test (or a group of similar tests) on fine-grained code level (e.g., classes, methods, and functions). Once a user changes one of the code elements, the test tool will automatically identify a set of tests to run, and those to skip as they are not impacted by changes. The tool is triggered by specific events and needs to be able to access various storage services to retrieve and store test dependencies. Early evaluations of the tool have shown a reduction of the testing budget by over 50%. 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.