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    • JavaScript
      Apache License 2.0
      0100Updated Dec 19, 2024Dec 19, 2024
    • Source code and data for the paper "Testing the Plasticity of Reinforcement Learning Based Systems"
      Python
      MIT License
      0200Updated Nov 17, 2024Nov 17, 2024
    • Replication package for the paper "Adversarial Testing with Reinforcement Learning: A Case Study on Autonomous Driving"
      Python
      MIT License
      0000Updated Nov 17, 2024Nov 17, 2024
    • muPRL

      Public
      Replication package for the paper "μPRL: a Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults" ICSE 2025.
      Python
      MIT License
      0200Updated Nov 17, 2024Nov 17, 2024
    • genbo

      Public
      Replication package for the paper "Boundary State Generation for Testing and Improvement of Autonomous Driving Systems"
      Python
      MIT License
      1000Updated Nov 17, 2024Nov 17, 2024
    • maxitwo

      Public
      Replication package of the paper "Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing"
      Python
      MIT License
      1300Updated Nov 17, 2024Nov 17, 2024
    • Replication package for the arxiv submission: "Adaptive Test Generation with Qgrams".
      Java
      MIT License
      0000Updated Oct 23, 2024Oct 23, 2024
    • deepjanus

      Public
      Tools and data of the paper "Model-based Exploration of the Frontier of Behaviours for Deep Learning System Testing"
      Python
      MIT License
      91500Updated Jul 9, 2024Jul 9, 2024
    • Python
      MIT License
      3410Updated Oct 31, 2023Oct 31, 2023
    • ambguess-src

      Public archive
      Python
      MIT License
      0000Updated Sep 23, 2023Sep 23, 2023
    • MIT License
      0000Updated Sep 23, 2023Sep 23, 2023
    • deepatash

      Public
      focused test generation for DL systems
      Python
      MIT License
      1101Updated Aug 28, 2023Aug 28, 2023
    • Uncertainty-Wizard is a plugin on top of tensorflow.keras, allowing to easily and efficiently create uncertainty-aware deep neural networks. Also useful if you want to train multiple small models in parallel.
      Python
      MIT License
      64525Updated May 24, 2023May 24, 2023
    • bisupervised

      Public archive
      TOSEM paper replication package
      PureBasic
      MIT License
      0100Updated May 24, 2023May 24, 2023
    • Python
      MIT License
      6900Updated May 4, 2023May 4, 2023
    • fashion-mnist-c

      Public archive
      Python
      MIT License
      1300Updated Feb 3, 2023Feb 3, 2023
    • dnn-tip

      Public
      A collection of dnn test input prioritizers often used as benchmarks in recent literature.
      Python
      MIT License
      21510Updated Feb 3, 2023Feb 3, 2023
    • unboxer

      Public
      An Empirical Study on Low- and High-Level Explanations of Deep Learning Misbehaviours
      HTML
      0100Updated Jan 11, 2023Jan 11, 2023
    • Python
      MIT License
      0000Updated Dec 9, 2022Dec 9, 2022
    • simple-tip

      Public archive
      This is the reproduction package of the paper Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning by M.Weiss and P.Tonella, published at ISSTA 2022
      Python
      MIT License
      3500Updated Jun 23, 2022Jun 23, 2022
    • surprise-adequacy

      Public archive
      Python
      MIT License
      01100Updated May 25, 2022May 25, 2022
    • A python library to generate out-of-distribution text datasets. Specifically, the library applies model-independent, commonplace corruptions (not model-specific, worst-case adversarial corruptions). We thus aim to allow benchmark-studies regarding robustness against realistic outliers.
      Python
      MIT License
      0100Updated Apr 26, 2022Apr 26, 2022
    • Python
      MIT License
      1500Updated Nov 12, 2021Nov 12, 2021
    • deepmetis

      Public
      Python
      MIT License
      0900Updated Aug 20, 2021Aug 20, 2021
    • 0100Updated Jul 2, 2021Jul 2, 2021
    • selforacle

      Public archive
      The code of our paper "Misbehaviour Prediction for Autonomous Driving Systems", including our improved Udacity simulator
      Python
      MIT License
      112200Updated Jun 30, 2021Jun 30, 2021
    • Repository for UROP 2021 exercise
      Python
      6000Updated Mar 3, 2021Mar 3, 2021
    • repli-ensemble-bench

      Public archive
      Python
      MIT License
      0000Updated Dec 21, 2020Dec 21, 2020
    • Source Code of the ICST 2021 paper "Fail-Safe Execution of Deep Learning based Systems through Uncertainty"
      Python
      MIT License
      0000Updated Dec 20, 2020Dec 20, 2020