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| title: Jira-To-Code Agent Environment | |
| emoji: 🛠️ | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: docker | |
| pinned: false | |
| license: mit | |
| app_port: 7860 | |
| # 🚀 Jira-To-Code: The Ultimate RL Coding Environment | |
| [](https://github.com/meta-pytorch/OpenEnv) | |
| [](https://opensource.org/licenses/MIT) | |
| **Jira-To-Code** is a high-fidelity Reinforcement Learning (RL) environment designed for the **Meta/Hugging Face OpenEnv Hackathon**. It challenges AI agents to act as Senior Software Engineers by resolving real-world Jira tickets within a secure, sandboxed codebase. | |
| --- | |
| ## 🌟 Key Features | |
| * **⚡ ReAct Ready**: Built-in support for Thought-Action-Observation loops. | |
| * **🧠 Episodic Memory**: Maintains full conversational history for multi-turn reasoning. | |
| * **📊 22 Diverse Tasks**: From simple bug fixes to complex architecture, concurrency, and security. | |
| * **📈 Rich Reward Shaping**: Partial credit for passing tests, step penalties for efficiency, and shaping rewards for active coding. | |
| * **🛡️ Robust Parsing**: Resilient JSON extraction and self-correction prompt injection. | |
| --- | |
| ## 🏗️ Environment Architecture | |
| Agents interact with the environment via a standardized FastAPI interface: | |
| | Action | Description | | |
| | :--- | :--- | | |
| | `list_files` | Explore the workspace directory structure. | | |
| | `read_file` | Read the content of a specific file. | | |
| | `write_file` | Create or overwrite code in the workspace. | | |
| | `run_tests` | Execute `pytest` and receive detailed traceback output. | | |
| | `submit` | Finalize the task and receive the definitive score. | | |
| --- | |
| ## 🎯 Available Tasks (22 Total) | |
| | Task ID | Level | Objective | | |
| | :--- | :--- | :--- | | |
| | `easy` | Easy | Fix off-by-one bug in `calculator.add()`. | | |
| | `easy_2` | Easy | Fix case-sensitivity bug in `string_utils.count_vowels()`. | | |
| | `easy_3` | Easy | API KeyError: use `.get()` with fallback for missing `phone_number`. | | |
| | `easy_4` | Easy | Off-by-One Pagination: Fix math index logic in `get_page_bounds`. | | |
| | `easy_5` | Easy | FastAPI Route Typo: Align `user_id` route param with function arg. | | |
| | `medium` | Medium | Implement `format_user_data()` dictionary mapping specs. | | |
| | `medium_2` | Medium | Implement complex `Email` and `Password` validation logic. | | |
| | `medium_3` | Medium | Missing Auth Middleware: Apply `@require_auth` to `/api/billing`. | | |
| | `medium_4` | Medium | ORM N+1 Problem: Rewrite fetches to use JOINs (`select_related`). | | |
| | `medium_5` | Medium | Regex Validation: Fix email regex to allow plus sign (`+`). | | |
| | `medium_6` | Medium | Error Handling: Add try/except fallback for currency rate timeouts. | | |
| | `medium_7` | Medium | Stale Cache: Add Redis invalidation to `update_user_profile`. | | |
| | `medium_8` | Medium | Timezone Naive: Make naive datetimes UTC aware. | | |
| | `medium_9` | Medium | State Machine: Add transition guards (CANCELLED -> SHIPPED). | | |
| | `medium_10` | Medium | Config Merge: Fix recursion logic for nested dict merges. | | |
| | `hard` | Hard | Implement `LRUCache` with $O(1)$ time complexity. | | |
| | `hard_2` | Hard | Implement `DirectedGraph` with BFS/DFS and Topological Sort. | | |
| | `hard_3` | Hard | Circular Dependency: Refactor `models/utils/config` via `base.py`. | | |
| | `hard_4` | Hard | Race Condition: Refactor threaded worker to use `queue.Queue`. | | |
| | `hard_5` | Hard | OOM Generator: Rewrite `readlines()` loop to use `yield` generators. | | |
| | `hard_6` | Hard | Implementation: Code `StripeGateway` matching `PaymentGateway` ABC. | | |
| | `hard_7` | Hard | Async Deadlock: Fix lock release safety using async context managers. | | |
| --- | |
| ## 🚀 Getting Started | |
| ### 1. Local Setup | |
| ```bash | |
| # Clone the repository | |
| git clone https://huggingface.co/spaces/Navigam/jira-to-code | |
| cd jira-to-code | |
| # Create and activate environment | |
| uv venv | |
| source .venv/bin/activate # Or .venv\Scripts\activate on Windows | |
| # Install dependencies | |
| uv pip install -e . | |
| ``` | |
| ### 2. Run Inference | |
| ```bash | |
| # Run the full baseline agent against all tasks | |
| uv run python inference.py | |
| # Run a specific task | |
| uv run python inference.py --tasks easy_2,medium | |
| ``` | |
| ### 3. Docker Deployment | |
| ```bash | |
| docker build -t jira-to-code . | |
| docker run -p 7860:7860 jira-to-code | |
| ``` | |
| --- | |
| ## 🛠️ Deep Dive: Design & Rubric Alignment | |
| ### 🎨 Creativity & Novelty | |
| * **Real-World Software Engineering Domain**: While most RL environments focus on games or simplified logic, **Jira-To-Code** provides a high-stakes, documentation-driven coding domain. Agents are forced to interpret edge cases from docstrings (e.g., case-insensitivity in vowel counting) just like real developers. | |
| * **Non-Sparse Reward Mechanics**: We move away from binary "Pass/Fail" signals. The environment rewards "Progress Toward Solution" by parsing intermediate test results. | |
| ### 📈 Reward Signal Design | |
| The environment provides a dense, informative reward signal to guide agent learning, ensuring all step scores are strictly continuously bounded: | |
| * **Strict Bounds (`0.01` to `0.99`)**: To comply with grading requirements, every step evaluated unconditionally maps to a boundary range strictly between 0 and 1. The literal 0.0 and 1.0 are actively bypassed. | |
| * **Action & Thinking Weightage**: The first 3 orientation steps (e.g., listing/reading files, thinking) receive a `+0.02` bonus shaping token to reward early planning. | |
| * **Efficiency Penalty**: For all steps beyond the 3rd step, a `-0.01` penalty is continuously applied to minimize rewards for agents taking excessively long. | |
| * **Linear Partial Credit**: Intermediate `run_tests` and the final `submit` rewards are calculated proportionally as `(passed_tests / total_tests)`. | |
| ### 🧱 Episode & Workspace Design | |
| * **Isolation & Reset**: Every `reset()` call generates a **cryptographically unique, isolated temporary directory**. This ensures the agent starts with a "Clean Slate" and prevents cross-contamination between tasks or episodes. | |
| * **Atomic Boundaries**: An episode concludes when the agent calls `submit` or reaches `MAX_STEPS`. | |
| * **Deterministic Grading**: Graders are based on hidden unit tests (`pytest`) that are immutable within the environment container, ensuring 100% reproducible scoring. | |
| --- | |
| ## 🏆 Scoring Rubric Alignment | |
| This environment is optimized for high marks in the OpenEnv Hackathon: | |
| * **Real-world Utility**: Models a developer's daily workflow. | |
| * **Task/Grader Quality**: Deterministic `pytest` grading with partial credit. | |
| * **Environment Design**: Gymnasium-style API with comprehensive observation space. | |
| * **Code Quality**: Passes `openenv validate` and follows strict Pydantic typing. | |
| --- | |
| ## 📜 License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |
| --- | |
| <div align="center"> | |
| <sub>Built with ❤️ for the OpenEnv Hackathon by <a href="https://huggingface.co/Navigam">Navigam</a></sub> | |
| </div> | |