One of the most effective ways to test complex language implementations, file systems, and other critical systems software is random test generation. This talk will cover a number of recent results that show how---despite the importance of hand-tooled random test generators for complex testing targets---there are methods that can be easily applied in almost any setting to greatly improve the effectiveness of random testing. Surprisingly, giving up on potentially finding any bug with every test makes it possible to find more bugs over all. The practical problem of finding distinct bugs in a large set of randomly generated tests, where the frequency of some bugs may be orders of magnitude higher than other bugs, is also open to non ad-hoc methods.
Alex Groce received his PhD in Computer Science from Carnegie Mellon University in 2005, and B.S. degrees in Computer Science and Multidisciplinary Studies (with a focus on English literature) from North Carolina State University in 1999. Before joining the Oregon State University faculty in 2009, he was a core member of the Laboratory for Reliable Software at NASA’s Jet Propulsion Laboratory, and taught classes on Software Testing at the California Institute of Technology. His activities at JPL included a role as lead developer and designer for test automation for the Mars Science Laboratory Curiosity mission's internal flight software test team, and lead roles in testing file systems for space missions. His research interests are in software engineering, particularly testing, model checking, code analysis, debugging, and error explanation and fault localization.
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