Integration of cargo-fuzz
to improve quality assurance of Rust code in Nyx
#162
Labels
Kind: Improvement
This is a proposed improvement
Priority: normal
Status: Design
Issue at Design phase of the quality assurance process
Milestone
High level description
Nyx, being a high fidelity astrodynamics software, is built with a focus on reliability and accuracy. To further enhance the robustness of the Rust codebase, I propose the integration of fuzz testing using the cargo-fuzz package.
Fuzz testing, or fuzzing, is a quality assurance technique used to discover coding errors and security loopholes in software. By sending random, unexpected, or malformed data as inputs to the software, we can identify potential crashes, memory leaks, or other vulnerabilities that may not be caught with traditional testing methods.
Requirements
The system needs to integrate the cargo-fuzz package into the existing Rust codebase. This will involve setting up the fuzzing environment, writing fuzz targets for critical parts of the code, and running the fuzz tests.
Test plans
The fuzz tests will be run as part of the continuous integration pipeline. The success of the integration will be measured by the ability of the fuzz tests to run without causing crashes or uncovering memory leaks. Edge cases to consider include handling of extremely large inputs, malformed data, and unexpected data types.
Design
The design will involve setting up the cargo-fuzz environment, identifying parts of the code that would benefit from fuzz testing, and writing the corresponding fuzz targets. The fuzz tests will then be integrated into the continuous integration pipeline.
Algorithm demonstration
The fuzzing process does not involve a change in algorithm but rather the introduction of a new testing method. The fuzzing process will be based on the cargo-fuzz package, which has been validated and is widely used in the Rust community.
API definition
The introduction of fuzz testing does not directly affect the Nyx APIs. However, the process may indirectly lead to improvements in the APIs if the fuzz testing uncovers issues that need to be addressed.
The integration of fuzz testing can be visualized as follows:
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