Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

[Feature]: Enhance testing by adding fuzzing

Abierto
  • ¿Algo no está bien?
#981 0 comentarios 1 reacción 1 asignado Ver en GitHub

Los mantenedores suelen responder en 1 día

@MacOS ya está trabajando en esto.

Desde el 13/8/2026.

Evaluación

Este issue todavía no se ha evaluado.

Descripción

enhancement

This issue is under active development and will be updated periodically based on the discussion below.

🧠 D: DOCUMENT — Problem & Opportunity

Currently, SKaiNET misses out an opportunity to systematically test it's functionality, and discover vulnerabilities, because it does not use fuzzing. This does matter for SKaiNET because it processes large amounts of data and is hence highly vulnerable for attacks. This issue proposes to add fuzzing.

Summary (1–2 sentences):

This issue aims to add fuzzing to it's tests.

🔍 A: ASSESS — Feasibility & Impact

Provide an evaluation of the proposal. Address the following:

✔️ Feasibility

Adding fuzzing to the tests is not straightforward to add because we have to settle on a third party library first, and then decide which parts of SKaiNET we want to test with fuzzing..

There are no architectural constraints because the issue pertains to tests.

✔️ Expected Impact

Adding fuzzing does neither - what it does, however, increase the probility of finding bugs.

✔️ Risks / Constraints

Neither of the three - we are not changing the library.

✔️ Dependencies

Adding fuzzing relies on a SKaINET module and requires a third-party library. We have to settle on both before we start.

📚 R: RESEARCH — What Must Be Understood First?

Document research tasks or open questions that must be answered before implementation:

Literature

MallisseryWu2023.pdf
ManèsEtAl2021.pdf
MillerEtAl1990.pdf
ZhaoZhang2018.pdf
ZhuEtAl2022.pdf

Research Tasks
  • Review existing frameworks (PyTorch, JAX, TensorFlow, etc.)
  • Identify relevant algorithms or formulas
  • Compare design patterns for modularity
  • Investigate numerically stable or optimized variants
  • Check for relevant academic papers or benchmarks
Open Questions

List unknowns that contributors should discuss or resolve.

  • Which module do we want to fuzzy test?
  • Which third-party library to we want to use for fuzzing?

🛠️ C: CODE — Implementation Plan

Break down actionable steps required to deliver this feature:

Development Tasks
  • Design module interface
  • Implement core functionality
  • Write unit tests (correctness + edge cases)
  • Add configuration integration
  • Update training loop or pipeline components (if applicable)
  • Add documentation & examples
  • Create or update benchmarks (optional)
  • Submit PR referencing this issue
Acceptance Criteria
  • Feature works as intended and passes all tests
  • Fully documented with clear examples
  • Follows SKaiNET coding style & modular design
  • Reviewed and approved by maintainers
  • No regressions introduced elsewhere

💬 Additional Notes

Add diagrams, pseudo-code, references, or implementation notes that may help future contributors.

Lenguaje dominante
Kotlin
Estrellas
52
Forks
15
Merge medio
1 d 15 h
PR fusionados (30 d)
36

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de SKaiNET-developers/SKaiNET

Todos los issues de SKaiNET-developers/SKaiNET

Issues similares

Más issues de Kotlin

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.