Merge pull request #1 from intelligent-control-lab/covered

merge code coverage badge

.github/workflows/CI.yml

@@ -59,3 +59,8 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
+      - name: Upload coverage reports to Codecov
+        uses: codecov/[email protected]
+        env:
+          token: ${{ secrets.CODECOV_TOKEN }}
+

README.md

@@ -2,6 +2,7 @@
 
 ![CI](https://github.com/intelligent-control-lab/ModelVerification.jl/actions/workflows/CI.yml/badge.svg)
 ![Doc](https://github.com/intelligent-control-lab/ModelVerification.jl/actions/workflows/documentation.yml/badge.svg)
+![codecov](https://codecov.io/gh/Louis2099/vnn2024/branch/master/graph/badge.svg?token=1HE6W2NURT)
 
 ## Introduction
 Deep Neural Network (DNN) is crucial in approximating nonlinear functions across diverse applications, such as computer vision and control. Verifying specific input-output properties can be a highly challenging task. To this end, we present [ModelVerification.jl](https://github.com/intelligent-control-lab/ModelVerification.jl), the only cutting-edge toolbox that contains a suite of state-of-the-art methods for verifying DNNs. This toolbox significantly extends and improves the previous version ([NeuralVerification.jl](https://sisl.github.io/NeuralVerification.jl/latest/)) and is designed to empower developers and machine learning practioners with robust tools for verifying and ensuring the trustworthiness of their DNN models.
@@ -134,4 +135,4 @@ Please consider citing this toolbox if it is useful for your research.
   howpublished = {\url{https://github.com/intelligent-control-lab/ModelVerification.jl}},
   year = {2024}
 }
-```
+```