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Your store Learn about the Publix Digital Coupon Program. But how to setup "random"? In fact, even "random" must be defined within some limits.

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For example, if you only feed a lossless compression algorithm with some random input, it will simply not be able to compress it, meaning you will always test the same code path. The way I've dealt with such issue for lz4 or zstd is to create programs able to generate "random compressible data", with some programmable characteristics compressibility, symbol variation, reproducible by seed.

And it helped a lot to test valid code path.

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The decompression side is more interested by resistance to invalid input. But even with random parameters, there is a need to target interesting properties to test.

Typically, a valid decompression stage is first run, to serve as a model. Then some "credible" fail scenarios are built from them. Zstd fuzzer tool typically tests : truncated input, too small destination buffer, and noisy source created from a valid one with some random changes, in order to bypass too simple screening stages.

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All these tests were extremely useful to strengthen the reliability of the code. But the idea that "random" was in fact defined within some limits make it clear that maybe some other code path, outside of limits of "random", may still fail if properly triggered. But how to find them? As stated earlier, brute force is not a good approach. There are too many similar cases which would be trivially reduced to a single code path.


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For example, the compressed format of zstd includes an initial 4-bytes identifier. A dumb random input would therefore have a 1 in 4 billion chances to pass such early screening, leaving little energy to test the rest of the code. For a long time, I believed it was necessary to know in details one's code to create some useful fuzzer tool.

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