fuzz | The fuzz typechecker for Z

As for example, I am using this website: https://sqliteonline.com/.

I try to fuzz around with queries and did as such:

I'm learning my way around fuzz testing C applications. As I understand it, most of the time when fuzzing, one has a C function that takes/reads files. The fuzzer is given a valid sample file, mutates it randomly or with coverage heuristics, and executes the function with this new input.

But now I don't want to fuzz a function that takes file inputs but a few functions that together make up an API. For example:

I'm trying to build libc6 with a custom prefix by modifying the prefix=/usr line in debian/rules. However, this fails because the patch is applied multiple times. Curiously, patching another file does not result in the same error. I've distilled the failure down to this script:

I have two large datasets. df1 is about 1m lines, and df2 is about 10m lines. I need to find matches for lines in df1 from df2.
I have posted an original version of this question separately. See here. Well answered by @laurent but I have some added specificities now. I would now like to:

1. Get the fuzz ratios for each of fname and lname in a column in my final matched dataframe

2. Write the code such that fuzz ratio for fname is set to >60, while fuzz ratio for lname is set to >75. In other words, a true match occurs if fuzz_ratio for fname>60 and fuzz ratio for lname>75; otherwise not a true match. A match would not be true if fuzz ratio for fname==80 while fuzz ratio for lname==60. While I understand that this can be done from (1) as a post-hoc filtering, it would make sense to do this at the stage of coding for a different matching.

I post here an example of my data. The solution by @laurent for the original problem can be found in the above link.

I have referred to this post but cannot get it to run for my particular case. I have two dataframes:

I am trying to use ImageMagick 7 to detect if a specific channel in an image is largely pure black and pure white (plus a little antialiasing, and there's a chance the image could be pure black). This is to distinguish from another kind of image that shares a naming convention but has photographic-like image data in the r/g/b channels.

(Basically both image types are specular maps from different engines. The one I'm trying to differentiate here is more modern and has the metallic map in the blue channel; the other is much older and just has the specular colour in the RGB channels and the gloss map in the alpha.)

Currently I'm comparing the channel to a clone of itself that has had a 50% threshold applied, using the AE metric to see if it's largely the same apart from a small amount of antialiasing, and a fuzz of 1% to account for occasional aberration from pure black/white. This command works, but of course at the moment it only returns the number of distorted pixels:

I'm not sure where I'm going wrong here and why my data is returning wrong. Writing this code to use fuzzywuzzy to clean bad input road names against a list of correct names, replacing the incorrect with the closest match.

It's returning all lines of data2 back. I'm looking for it to return the same, or replaced lines of data1 back to me.

My Minimal, Reproducible Example:

In RSpec I would use the following to obtain a Random which is seeded from the test order random seed. This would give us a reproducible RNG for things like fuzzing:

Here is the problem: I want to define function which will compare string ratios using fuzzy.ration() within 2 lists (not same size). It should return entities from list 1, which have at least one ratio bigger than 60 compared with second.