Automata Models for Effective Bug Description

Zenodo DOI: 10.5281/zenodo.15798675

The artifact contains all experiments presented in the paper.

When preparing the artifact we used:

• macOS 11.7.5
• Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
• memory: 16 GB
• disk: 256 GB

Running a complete experiment in each evaluation takes multiple days and requires huge resources. We included instructions for a scaled-down evaluation for each experiment table, which takes several minutes

Installation and Usage

To run all experiments, the first step is to pull the docker image from dockerhub:

docker pull tomyaacov/automata-bug-description-docker

or download it from the artifact url and load it:

docker load -i <path to image>/automata-bug-description-docker.docker

and then run it:

docker run -it tomyaacov/automata-bug-description-docker

You might need to run the command with sudo if you get a permission error while running the docker commands. For example:

sudo docker run -it tomyaacov/automata-bug-description-docker

When running the container, the current directory will be Papers-2025-MODELS-Automata-Bug-Description.

To run the evaluation, run the main.py file with the following arguments:

• -B is the benchmark name (one of m24, m45, m54, m55, m76, m95, m135, m158, m159, m164, m172, m181, m183, m185, m201, m22, m27, m41, m106, m131, m132, m167, m173, m182, m189, m196, m199, all)
• -T is the number of system configuration with the following options:
  • 1 for the UNR setup (Table 1 in the paper)
  • 2 for the FDR setup (Table 2 in the paper)
  • 3 for the ADR setup (Table 3 in the paper)

For example, to run the evaluation for the benchmark m164 with the UNR setup, run:

python main.py -B m164 -T 1

warning - this can take few seconds

The results will be saved in a csv file in the output folder. For example the above run results will be saved in output/results_m164_T1_rpni.csv.

More examples:

to run the evaluation for the benchmark m164 with the FDR setup, run:

python main.py -B m164 -T 2

warning - this can take few seconds

results will be saved in output/results_m164_T2_rpni.csv.

to run the evaluation for the benchmark m164 with the ADR setup, run:

python main.py -B m164 -T 3

warning - this can take few seconds

results will be saved in output/results_m164_T3_rpni.csv.

To run all benchmarks, set -B all.

warning - this my take more than a day to run

for example, to run all benchmarks with the UNR setup, run:

python main.py -B all -T 1

The results will be saved in the output folder with the prefix results_all_T1_rpni.csv.

Tips on docker termination and resources cleanup

To terminate the docker container, you can use exit command in the terminal where the container is running.

To remove a docker container, you can use the following command:

docker rm -f <container_id>

To remove the docker image, you can use the following command:

docker rmi tomyaacov/automata-bug-description-docker