Yang Chen, Alex Groce, Chaoqiang Zhang, Weng-Keen Wong, Xiaoli Fern, Eric Eide, and John Regehr

Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) 2013.

DOI: 10.1145/2491956.2462173

© Copyright 2013 by ACM, Inc. Posted by permission of ACM; the copies posted here may not be redistributed. The definitive copy of this work will be available from the ACM Digital Library.

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areas
Languages, Software Testing

abstract

Aggressive random testing tools (“fuzzers”) are impressively effective at finding compiler bugs. For example, a single test-case generator has resulted in more than 1,700 bugs reported for a single JavaScript engine. However, fuzzers can be frustrating to use: they indiscriminately and repeatedly find bugs that may not be severe enough to fix right away. Currently, users filter out undesirable test cases using ad hoc methods such as disallowing problematic features in tests and grepping test results. This paper formulates and addresses the fuzzer taming problem: given a potentially large number of random test cases that trigger failures, order them such that diverse, interesting test cases are highly ranked. Our evaluation shows our ability to solve the fuzzer taming problem for 3,799 test cases triggering 46 bugs in a C compiler and 2,603 test cases triggering 28 bugs in a JavaScript engine.