Dodona: Automated Oracle Data Set Selection
Software complexity has increased the need for automated software testing. Most research on automated testing, however, has focused on creating test input data. While careful selection of input data is necessary to reach faulty states in a system under test, test oracles are needed to actually detect failures. In this work, we describe Dodona, a system that supports the generation of test oracles. Our empirical study of Dodona reveals that it is more effective and efficient than the current state-of-the-art approach for generating oracle data sets, and can often yield oracles that are almost as effective as oracles hand-crafted by engineers without support.
Bio: Matt Staats has worked as a research associate at the Software Verification and Validation lab at the University of Luxembourg and at the Korean Institute of Science and Technology in Daejeon, South Korea. He received his PhD from the University of Minnesota - Twin Cities. Matt Staat's research interests are realistic automated software testing and empirical software engineering. He is currently employed by Google Inc., applying hard won knowledge on software testing to real world systems.