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On the Impact of Hitting System Resource Limits on Test Flakiness

This repository contains the code for the FTW paper "On the Impact of Hitting System Resource Limits on Test Flakiness" (preprint). We also provide step by step instructions on how to expand the provided dataset, available on FigShare.

Generating Aggregated Statistics File from a Project

Retrieving the Data

  1. Copy the GitHub Workflow file workflow.yml into the projects you want to include. To be recognized by GitHub, it needs to be put in that project's folder .github/workflows/.
  • In the workflow file:
    • change <path-to-proj.xcodeproj> with the actual path to the project file (or workspace).
    • change <scheme> to the name of the scheme in the project containing the UI Tests. If not present, open the project, create a new scheme and select only the UI Tests' target.
See screenshots ![new-scheme](images/new-scheme.jpg) ![target-scheme](images/target-scheme.jpg)
  • Push the changes to GitHub. GitHub will recognize the workflow and start running the UI Tests. You can access the run in the Actions tab of the GitHub Project.
  • Once finished, access the workflow's details.
    • Download the artifact named raw-stats containing a json file with all resource measurements. Unzip and save this file in the folder data.
    • Click on the workflow's job ui-tests-run and "View the raw logs" to export the logs file. Save the file as logs.file and put it data.
See screenshots ![view-raw-logs](images/view-raw-logs.jpg)

Generating the Aggregated Statistics Dataset

cd into parse-logs and execute npm run build; npm run run. A json file containing the aggregated data will be generated at data/aggregated-stats.json.

If you have multiple projects you can merge the json arrays in each aggregated-stats.json into one.

Evaluating the Dataset

  1. Make sure to have a aggregated-stats.json file in data. This could contain the data from a single or multiple projects.
  2. install the required python libraries: pip install -r requirements.txt.
  3. Open dataset-analysis.ipynb.
  4. Execute the Jupyter Notebook. If you do not have LaTeX installed remember to update the respective variable inside of the notebook to generate the plots.