Test cases play an important role in testing and debugging software. Smaller tests are easier to understand and use for these tasks. Given a test that demonstrates a bug, test case reduction finds a smaller variant of the test case that exhibits the same bug. Classically, one of the challenges for test case reduction is that the process is slow, often taking hours. For hierarchically structured inputs like source code, the state of the art is Perses, a recent grammar aware and queue driven approach for test case reduction. Perses traverses nodes in the abstract syntax tree (AST) of a program (test case) based on a priority order and tries to reduce them while preserving syntactic validity. In this paper, we show that Perses' reduction strategy suffers from priority inversion, where significant time may be spent trying to perform reduction operations on lower priority portions of the AST. We show that this adversely affects the reduction speed. We propose Pardis, a technique for priority aware test case reduction that avoids priority inversion. We implemented Pardis and evaluated it on the same set of benchmarks used in the Perses evaluation. Our results indicate that compared to Perses, Pardis is able to reduce test cases 1.3x to 7.8x faster and with 46% to 80% fewer queries.

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Given a failing test case, test case reduction yields a smaller test case that reproduces the failure. This process can be time consuming due to repeated trial and error with smaller test cases. Current techniques speed up reduction by only exploring syntactically valid candidates, but they still spend significant effort on semantically invalid candidates. In this paper, we propose a model-guided approach to speed up test case reduction. The approach trains a model of semantic properties driven by syntactic test case properties. By using this model, we can skip testing even syntactically valid test case candidates that are unlikely to succeed. We evaluate this model-guided reduction on a suite of 14 large fuzzer-generated C test cases from the bug repositories of two wellknown C compilers, GCC and Clang. Our results show that with an average precision of 77%, we can decrease the number of removal trials by 14% to 61%. We observe a 30% geomean improvement in reduction time over the state of the art technique while preserving similar reduction power.

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