TianoShield Survey — Exploratory Data Analysis (EDA) Notebooks

This repository contains the Jupyter notebooks used for the exploratory data analysis (EDA) of the TianoShield survey on security practices and preferences within the TianoCore open-source community and the UEFI ecosystem.

The survey was conducted between March 8–25, 2026, as part of the NSF-funded TianoShield project (Grant No. 2534021), and yielded 13 valid responses after preprocessing.

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Repository Structure

Notebook	Section	Topics Covered
eda_01_demographics.ipynb	Demographics	Sex/gender, age group, country, highest degree
eda_02_project_info.ipynb	Project Information	Ecosystem role, project focus, team size, experience, codebase size, project stage
eda_03_bug_management.ipynb	Bug Management	Issue tracking, bug prioritisation, triage, upstreaming conditions and methods, lifecycle maturity
eda_04_patch_management.ipynb	Patch Management	Patch propagation, semantic patching, upstream submission frequency
eda_05_testing_analysis.ipynb	Verification & Validation	Formal verification, static analysis, CodeQL, dynamic analysis, Intel HBFA
eda_06_security.ipynb	Firmware Security	Security bug priority rankings, bug bounty programmes, security effort, TianoCore posture
eda_07_memory_safety.ipynb	Memory Safety & Rust	Barriers to adoption, interest in Rust for EDK II, in-house Rust components
eda_08_devops.ipynb	DevOps / DevSecOps	CI/CD pipeline adoption, DevSecOps knowledge
eda_09_supply_chain.ipynb	Supply Chain	Verification methods, challenges in supply-chain visibility
eda_10_impact.ipynb	Organisational Impact & Barriers	Vulnerability impact types, adoption barriers, support needs, person-hours

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How to Run

These notebooks are designed to run in Google Colab.

Prerequisites

1. Mount your Google Drive and place the cleaned survey CSV at:

   /content/drive/MyDrive/TS-Survey/tianocore-survey_cleaned.csv
   

2. Figures will be saved to:

   /content/drive/MyDrive/TS-Survey/figures/
   

Steps

1. Upload a notebook to Google Colab
2. Run Step 1 — Mount Google Drive
3. Run Step 2 — Install dependencies (matplotlib, pandas)
4. Run Step 3 — Load data and helpers
5. Run the remaining cells for each question

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Data

The survey data is available at Dataverse.

Paper

These notebooks support the following paper:

N. Siavash, C. Glosner, A. Sharma, B. Trinkenreich, T. E. Boult, A. Machiry, and A. Moin, “An empirical study of the TianoCore community,” to be presented at the International Workshop on Firmware Testing and Analysis (FTA), part of SPLASH/ISSTA 2026.

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Acknowledgements

This material is based upon work supported by the U.S. National Science Foundation (NSF) under Grant No. 2534021. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.