Rust port of Paul Uszak's new randomness testing suite for TRNG Makers
generating in the sub 1 MB space. Successor to John Walker’s venerable `ent`.
Pipe binary entropy data into the `onod3000` command line tool or call it
with `-f <filename>` to load entropy from a file.
```
`Onod3000` provides multiple randomness tests, each outputting a statistic, a z-score andd a p-value to assess the conformity of the data to random behavior.
All tests have been carefully ported from [Paul Uszak's Java code](http://www.reallyreallyrandom.com/gitbucketlabhub/) (ent3000-0.6.0-beta). Java libraries were substituted with Rust crate equivalents when possible. The suite includes the following tests:
- **Monobit Test**: Evaluates the balance of 0s and 1s.
- **Chi-Square Tests**: Tests the uniformity of bits and bytes.
- **MeanByte Test**: Checks if the mean byte value aligns with expected randomness.
- **Kolmogorov-Smirnov (KS) Test**: Assesses uniformity of data distribution.
- **Pi Test**: Uses Monte Carlo methods to approximate π.
- **Shells Test**: Analyzes distances in 3D space.
- **Gaps Test**: Measures gaps between occurrences of a specific value.
- **Avalanche Test**: Analyzes bit-level changes in data chunks.
- **Runs and RunUps Tests**: Checks for sequential patterns in data.
- **Prediction Test**: Assesses the predictability of next bits.
- **UnCorrelation Test**: Evaluates correlation between shifted data.
More details on some individual tests [available here](http://www.reallyreallyrandom.com/ent3000/the-tests/index.html)
To test the implementation three files of binary random data have been analyzed using the original Java program (version 0.6.0-beta) and this Rust port, expecting the same p-values. The test files can be found in the test folder.
Although the implementation of the randomness tests closely follows the original Java logic, differences in the results arise in the Kolmogorov-Smirnov test. The external libraries used for running the test probably differ in their implementation. Also the uniform distribution to test against is provided by the Apache Commons Math library. Since it would be way beyond the scope of this porting project to try to fully match the functionality of the dependencies used we just accept the minor difference. Another issue might be that Java emphasizes predictability and portability, enforcing strict IEEE 754 behavior across platforms. Rust on the other hand prioritizes performance and flexibility, allowing platform-specific optimizations that may deviate slightly from strict IEEE semantics.
We implemented a Well Equidistributed Long-period Linear pseudo-random number generator that is used with a random seed derived from an epoch timestamp that is being used as uniform distribution for the KS test.
This work was made possible with the invaluable assistance of OpenAI's ChatGPT, which provided guidance, debugging help, and inspiration throughout the development process.
