<div align="center">

🧪 AITestCaseGen

AI-powered test case management toolkit for Jira Zephyr Scale

Generate, import, update, link, and report on test cases — fully automated, with or without cloud AI.

</div>

---

🚀 Why AITestCaseGen?

Writing test cases manually from requirements is one of the biggest time sinks in QA. AITestCaseGen reads your requirements — from Jira, Confluence, documents, or plain text — and generates structured positive, negative, and edge-case test cases, then pushes them directly into Zephyr Scale.

It's not just a generator. It's a full test case lifecycle CLI:

| | | |---|---| | 🧠 Generate | Create test cases from Jira issues, Confluence pages, docs, or text | | 📥 Import | Push generated cases directly into Zephyr Scale | | ✏️ Update | Bulk-edit existing test cases | | 🔗 Link | Connect test cases to Jira issues | | 🗑️ Delete | Remove test cases (with dry-run safety) | | 📤 Export | Pull existing cases out of Zephyr | | 🔄 Test Cycles | Update execution results in bulk |

---

🤖 AI Engine Priority

The script automatically picks the best available engine — zero config required:

| Priority | Engine | Condition | |---|---|---| | 1 | HuggingFace | transformers installed + model downloadable | | 2 | Ollama | Ollama installed and running locally | | 3 | Rule-based | Always works — no AI needed |

This means the tool never hard-fails — even with no internet, no API key, and no GPU.

---

✨ Why This Is Different

Most "AI test case generator" tools are tightly coupled to a single cloud provider — if your API key expires or your org blocks external LLM calls, the tool stops working entirely. AITestCaseGen is built around graceful degradation:

• No hard dependency on a paid API — HuggingFace models run via your Artifactory mirror or locally, Ollama runs fully offline, and the rule-based engine guarantees the script keeps working even with zero AI infrastructure.
• Enterprise-friendly by default — designed for locked-down environments with no access to OpenAI/Anthropic APIs; works fully air-gapped.
• End-to-end lifecycle, not just generation — generates and imports, updates, deletes, links, exports, and updates test cycle executions directly via the Zephyr Scale REST API.
• Multi-source input — Jira issues, Confluence pages, document folders (.docx / .pdf / .txt), or raw text all feed the same pipeline.

---

📊 Comparison with Similar Approaches

| Capability | AITestCaseGen | ChatGPT / Copy-Paste | Commercial Zephyr AI Add-ons | |---|---|---|---| | Works offline / air-gapped | ✅ Ollama or local model | ❌ Cloud API required | ❌ Usually cloud-only | | No paid API key required | ✅ Rule-based fallback | ❌ Subscription needed | ❌ License cost | | Direct Zephyr Scale integration | ✅ Full API pipeline | ❌ Manual copy-paste | ✅ Vendor UI only | | Multi-source input | ✅ Jira, Confluence, docs, text | ⚠️ One source at a time | ⚠️ Often limited | | Bulk test cycle updates | ✅ CSV/Excel bulk update | ❌ Not supported | ⚠️ Vendor-dependent | | Open source & customizable | ✅ Edit anything | N/A | ❌ Closed source | | CI/CD friendly | ✅ Pure CLI | ❌ Manual workflow | ⚠️ Varies | | Cost | Free | Subscription | License/subscription |

The core differentiator: AI generation is just one stage of a connected pipeline — generation, Jira/Zephyr sync, and execution reporting all flow together instead of ending at "here's some text, paste it yourself."

---

📁 Project Structure

AITestCaseGen/
├── resources/
│   ├── config.ini                    # Jira credentials and settings
│   ├── import_test_case.xlsx         # Input for import / output of AI generator
│   ├── update_test_case.xlsx         # Input for update script
│   ├── delete_Test_case.xlsx         # Input for delete script
│   └── update_test_cycle.xlsx        # Input for test cycle update
├── src/
│   ├── main/
│   │   ├── config.py                 # Reads config.ini
│   │   └── jira_client.py            # Jira / Zephyr Scale REST API client
│   └── test/
│       ├── generate_tc_via_AI.py     # AI test case generator
│       ├── import_testcases.py       # Import test cases to Zephyr Scale
│       ├── update_testcases.py       # Update existing test cases
│       ├── delete_testcases.py       # Delete test cases
│       ├── link_testcases_to_jira.py # Link test cases to Jira issues
│       ├── export_test_cases.py      # Export test cases from Zephyr
│       ├── project_info.py           # Display Jira project information
│       ├── update_test_cycle.py      # Update test cycle execution results
│       └── create_excel_templates.py # Generate blank Excel input templates
└── requirements.txt

---

⚙️ Prerequisites

• Python 3.10+
• Install dependencies:

pip3 install -r requirements.txt
pip3 install transformers accelerate huggingface_hub

---

🔧 Configuration

Edit resources/config.ini:

[jira]
base_url   = https://your-jira-instance.com
jira_token = YOUR_JIRA_PAT_TOKEN
project_key = YOUR_PROJECT_KEY
project_id  = YOUR_PROJECT_ID

[testcase]
owner_account_id = YOUR_ACCOUNT_ID
test_cycle       = YOUR_CYCLE_KEY

[ai]
hf_token    = YOUR_HUGGINGFACE_API_TOKEN
hf_endpoint = https://your-artifactory-host/artifactory/api/huggingfaceml/huggingface

[defaults]
type_of_test      = Regression Test
automation_status = Manual

🔒 config.ini is in .gitignore — credentials are never committed to git.

---

🧩 Setup — HuggingFace (Primary)

Step 1 — Install dependencies

pip3 install transformers accelerate huggingface_hub

Step 2 — Configure HuggingFace Token

1. Get your HuggingFace or Artifactory identity token
2. Add it to resources/config.ini:

[ai]
hf_token = your-artifactory-identity-token

Step 3 — First Run (model download ~300MB)

The model downloads automatically on first run from your configured hf_endpoint.

Default model: google/flan-t5-base

| Model | Size | Speed | |---|---|---| | google/flan-t5-small | ~80MB | Fastest | | google/flan-t5-base | ~300MB | Default | | google/flan-t5-large | ~800MB | Most accurate |

---

🦙 Setup — Ollama (Optional Fallback)

If HuggingFace is unavailable, install Ollama for fully local AI generation:

1. Download: https://ollama.com/download/windows
2. Run OllamaSetup.exe
3. Pull a model:

ollama pull llama3.2

No code changes needed — the script detects Ollama automatically.

---

📝 Generating Test Cases

From a folder of documents (.docx / .pdf / .txt)

python -m test.generate_tc_via_AI --input-folder "C:\path\to\docs" --folder "/Generated" --count 10

From a Jira issue key

python -m test.generate_tc_via_AI --issue PROJECT-1234 --folder "/Generated" --count 5

From a Jira issue + documents folder (combined)

python -m test.generate_tc_via_AI --issue PROJECT-1234 --input-folder "C:\path\to\docs" --folder "/Generated" --count 10

From a single file

python -m test.generate_tc_via_AI --file resources/requirements.txt --folder "/Generated"

From a Confluence page

python -m test.generate_tc_via_AI --confluence 123456789 --folder "/Generated" --count 10

Pass the page ID (numeric) or the full page URL:

python -m test.generate_tc_via_AI --confluence "https://your-jira.com/wiki/spaces/PROJECT/pages/123456789" --count 10

Multiple pages (comma-separated):

python -m test.generate_tc_via_AI --confluence "123456789,987654321" --folder "/Generated"

How to get the Confluence page ID: Open the page → click ... (More actions) → Page Information → ID is in the URL: .../pages/<pageId>/...

From inline text

python -m test.generate_tc_via_AI --text "User should be able to login with valid credentials" --count 5

All CLI Options

| Argument | Description | Default | |---|---|---| | --issue | Jira issue key (e.g. PROJECT-1234) | — | | --input-folder | Folder containing .docx / .pdf / .txt files | — | | --file | Single requirements file path | — | | --text | Inline requirement text | — | | --confluence | Confluence page ID or URL (comma-separated) | — | | --model | HuggingFace or Ollama model name | google/flan-t5-base | | --count | Number of test cases to generate per source | 5 | | --folder | Zephyr Scale folder path | /Generated | | --output | Output .xlsx file path | resources/import_test_case.xlsx |

---

📤 Import Generated Test Cases to Jira

python -m test.import_testcases --file ..\resources\import_test_case.xlsx

---

🛠️ Other Scripts

Update existing test cases

python -m test.update_testcases --file ..\resources\update_test_case.xlsx

Delete test cases — dry run first, then live

python -m test.delete_testcases --file ..\resources\delete_Test_case.xlsx --dry-run
python -m test.delete_testcases --file ..\resources\delete_Test_case.xlsx

Link test cases to a Jira issue

python -m test.link_testcases_to_jira --file ..\resources\update_test_case.xlsx

Update test cycle execution results

python -m test.update_test_cycle --file ..\resources\update_test_cycle.xlsx

🤝 Contributing

Issues, feature requests, and PRs are welcome. If AITestCaseGen saves you time, consider giving it a ⭐ — it helps others discover the project.

📜 License

MIT License — see LICENSE for details.