traceflow-rtm 0.0.28

A Python library for managing software development documentation and testing that automates the generation of PDFs, validation packs, and traceability matrices.

TraceFlow

TraceFlow is a Python library designed to help manage software development documentation and testing processes, streamlining the production of requirements, design documents, and manual test cases. With built-in support for PDF export, you can easily sign the output making TraceFlow particularly suited for projects in regulated environments or environments where compliance with 21 CFR 11 is necessary.

By leveraging TraceFlow, you can:

• Maintain your documentation in the same git repository as your code, ensuring version control and easy collaboration.
• Automatically produce a "validation pack" containing all requirements, design, and test plans in PDF format.
• Generate a traceability matrix linking all requirements to all tests.
• Capture structured risk registers with requirement/test cross-links and colour-coded pre/post-mitigation risk levels.
• Create fillable PDF forms for manual tests.
• The result will look something like this

Getting Started

To use TraceFlow, organize your Markdown files in the following folder structure:

project/
  ├── requirements/
  │   ├── requirements.md
  │   └── ...
  ├── design/
  │   ├── design.md
  │   └── ...
  ├── risks/
  │   ├── risk-register.md
  │   └── ...
  └── tests/
      ├── test_plan.md
      └── ...

Create your Markdown files based on the provided examples:

• Requirements Document Example
• Test Plan Example with Manual Test
• Design Document Example

Running TraceFlow

With your Markdown files in place, you can run TraceFlow using the following command:

traceflow path/to/project-docs 1.0.0 project.pdf

The second argument is the version string that will be shown on the report (change it to whatever release identifier you need).

Two optional arguments let you replace the logos that appear in the header (top-left) and footer (top-right):

• --top-left-logo (alias --traceflow-logo) points to the image shown in the top-left of every page (header).
• --top-right-logo (alias --voxelflow-logo) points to the image shown in the bottom-left of every page (footer).

If you do not pass these options, TraceFlow falls back to the packaged images in traceflow/res/.

traceflow path/to/project-docs 1.0.0 project.pdf --top-left-logo assets/traceflow.png --top-right-logo assets/voxelflow.png

This command will generate a PDF for all of your documentation and test cases, as well as a validation pack and traceability matrix.

Append --individual-pdfs to also emit per-section PDFs alongside the combined validation pack. For example, running traceflow traceflow 1.0.0 1corelab-validation-pack.pdf --individual-pdfs will produce the main PDF plus files like 1corelab-validation-pack-design.pdf, 1corelab-validation-pack-docs.pdf, 1corelab-validation-pack-risks.pdf, 1corelab-validation-pack-requirements.pdf, and 1corelab-validation-pack-tests.pdf when those sections exist in your source documents.

Running the example

TraceFlow includes the examples directory with requirements, design, risks, and tests. To reproduce that example, run:

uv run traceflow examples 1.0.0 test.pdf --test-results-dir example-test-results

On macOS you may need to prefix the command with DYLD_FALLBACK_LIBRARY_PATH=/opt/homebrew/lib so the bundled cairosvg finds libcairo. This example writes test.pdf into the current directory and stores temporary LaTeX artifacts in report/.

Using autotest blocks

You can embed test evidence captured outside TraceFlow by pointing to a test-results folder:

• Pass --test-results-dir PATH when running TraceFlow.
• In your markdown test plan, use:

TEST-ID

TraceFlow resolves TEST-ID to PATH/TEST-ID/result.md and renders that markdown content into the PDF. If PATH/TEST-ID or result.md is missing, TraceFlow raises an error.

Expected folder structure:

test-results/
  TEST-ID/
    result.md
    screenshots/
      screenshot-001.png
      ...

Requirements

# Requirements

## REQ-001: User authentication

Users must be able to authenticate using their email address and a password.

### Example LaTeX equation

The strength of the user's password should follow the entropy equation:

$$ H = L \times \log_2(N) $$

Where $H$ is the entropy, $L$ is the password length, and $N$ is the number of possible symbols.

## REQ-002: MRI dataset import

The platform must support importing MRI datasets in DICOM format.

### Example image (.png)

![Caption for example image](./mri_sample.png)

## REQ-003: Image analysis pipeline

- The platform should provide a Python API for building image analysis pipelines.
- The pipelines must be able to process MRI datasets in a compliant way.

### Example table

| Step          | Description                                      | API Function       |
|---------------|--------------------------------------------------|--------------------|
| Preprocessing | Remove noise, artifacts, and normalize intensity | preprocess_data()  |
| Segmentation  | Segment the relevant regions of interest         | segment_roi()      |
| Feature extraction | Extract features from segmented regions      | extract_features() |
| Classification | Classify the extracted features                 | classify_data()    |

### Example flow chart (using mermaid)

\```mermaid
%% caption: Example analysis pipeline
graph LR
A[Preprocessing] --> B[Segmentation]
B --> C[Feature extraction]
C --> D[Classification]
\```

Test Plan

## TEST-001: User authentication

**Requirement ID:** REQ-001

### Test Steps:

1. Navigate to the login page.
2. Enter a valid email address and password.
3. Click the "Login" button.

### Expected Result:

The user is logged in and redirected to the main dashboard.

### Test Result (Manual Sign-off):

\```manualtest
\```

### Test Result (Automatic Evidence):

\```autotest
TEST-002
\```

Design Document

## User Authentication

To address **REQ-001**, we will implement an authentication system using JWT (JSON Web Tokens). The system will include the following components:

- A login page with input fields for email and password
- A backend API endpoint to validate user credentials
- Middleware to validate JWT tokens for accessing protected resources

## MRI Dataset Import

To address **REQ-002**, we will develop a module to import MRI datasets in DICOM format. The module will include:

- A function to parse DICOM files
- Error handling for unsupported or malformed files
- Integration with the existing data storage system

## Image Analysis Pipeline

To address **REQ-003**, we will create a Python API for building and executing image analysis pipelines. This API will include:

- A set of Python classes to represent pipeline components
- Functions to connect and execute pipeline components
- Compliance checks to ensure the pipeline adheres to the required standards

Risk Register Example

TraceFlow now understands risk registers and renders them on A3 landscape pages with colour-coded severity, probability, and residual risk ratings.

# Risk Register

## RISK-001: Incorrect study-patient association

Hazardous Situation: Clinician views wrong patient's images believing they are correct
Harm: Misdiagnosis, inappropriate treatment
Cause: Race condition during HL7/DICOM message processing leading to incorrect PatientID or AccessionNumber assignment
Severity: High
Probability: Medium
Controls: Unit/integration tests for identifier logic (REQ-005, TEST-002)
Residual Severity: Medium
Residual Probability: Low
Residual Risk: Operator review plus automatic quarantine when mismatches occur

Generic Document Example

Any additional Markdown files (e.g., Installation/User Specifications) are included verbatim and can link to requirements, tests, or risks using their IDs:

# Installation & User Specification

- Execute TEST-003 and record the release tag.
- Confirm the identifier reconciliation feature (REQ-005) is active before go-live.
- Review RISK-001 and RISK-002 residual risk statements with the clinical safety officer.