Random testing library for C#
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Updated
Oct 7, 2026 - C#
Random testing library for C#
A metamorphic testing library for Zig
Prove an AI-written fix actually fixes the bug. mini-ork certify reproduces the bug against your repo's own code, attacks the patch with generated invariants in a Docker sandbox, and returns PROVEN / REFUTED / UNVERIFIED with a JSON certificate. Part of a verifier-gated, multi-model agent runtime.
Automated testing tool to find logic bugs in graph database systems
A barebones utility for metamorphic testing in Rust
This is the project repository of our ASE22 paper: Natural Test Generation for Precise Testing of Question Answering Software
Property-based testing and metamorphic testing combinator library for Gleam
Metamorphic testing for AI outputs — find wrong LLM answers without an answer key
Squeeze your model with pressure prompts to see if its behavior leaks.
This repository contains examples of Morphy test specifications and experiment data.
Detecting Relational Boundary Erosion in AI systems. A framework for testing whether models maintain honest, calibrated, and appropriate boundaries.
This Actor demonstrates the Apify metamorph operation by processing multiple URLs with configurable delays, then transforming into another Actor to complete the work in a single continuous run.
Is your RAG understanding your data - or guessing?
Never confidently wrong: a TLA+-verified consensus kernel around TypeSafe's Jev, run through 1,680 chaos-tested pharmacy decisions with zero wrong verdicts. Film, code, and every captured call.
Research benchmark measuring whether an authorized synthetic cyber capability keeps working as its environment changes — independently verified, policy-gated, and adaptive.
An official implementation for "Fairness Evaluation in Deepfake Detection Models using Metamorphic Testing"
Autonomous Multi-Turn Agentic Metamorphic Fuzzer & Swarm Consensus Stress-Testing Engine. Formulates Martingale context drift estimators, metamorphic execution DAGs, and Sybil groupthink inoculation filters.
Adversarial patch verification with executable evidence, minimized counterexamples, regression synthesis, and CI/VS Code integrations.
rudimentary algorithm for grouping objects based on traits
⚡ Developed Code for the New Talents in Quantum Technologies Program of the Gulbenkian Foundation / Capstone Project @FEUP
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