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Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring (Replication-Package)

This folder contains the source code for the reproduction of our empirical studies, presented in:

Michael Weiss and Paolo Tonella, Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring, IEEE International Conference on Software Testing, Verification and Validation 2021 (ICST).

To get access to uncertainty_wizard (the tool presented in the paper), please refer to the library github repo.

Limitations and notes:

  • These exeperiments were executed using a very early version of uncertainty-wizard, pre-dating public releases. The dependency is thus not pip-downloadable. However, the code should be mostly compatible with uncertainty-wizard v0.1.0. We are happy to provide the sources of the (non-public) early version 0.0.14 upon request.
  • Some of the used datasets (Traffic and ImageNet) need to be downloaded directly from their original sources (copyright reasons).
  • Make sure to mount a drive root/assets to save the results and datasets
  • For windows, install a venv according to the requirements.txt
  • For linux, we recommend building a docker container according to the docker file in docker/gpu/env/Dockerfile. Optionally, this can be used to automatically create the docker container:

    bash ./scripts/docker_build.sh -g emp_uncertainty.

  • Some paths and system config (e.g. gpu selection) are workstation specific and you may have to change them.
  • This repository is archived (i.e., we are not planning to modify the replication package in the future). For questions about the replication package, do not hesitate to contact us by email. For questions regarding uncertainty-wizard we refer to the issue tracker in the library github repo.