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0135a17 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | # PROJECT.md — Bug Triage OpenEnv Environment
> **Round 1 opens:** April 1, 2026 | **Deadline:** April 7, 2026, 11:59 PM IST
---
## Problem Statement
Software teams receive hundreds of bug reports daily. Manually triaging each report —
classifying the bug type, assigning a priority level, routing to the right developer,
and deciding the appropriate action — is slow, error-prone, and a significant cost
to engineering velocity.
**This environment** trains an LLM agent to perform **automated bug triage** at
production scale, simulating real-world issue tracking systems like Jira and GitHub
Issues. The agent reads raw bug reports (title, description, stack traces, metadata)
and must classify, prioritize, and route each issue accurately.
---
## Real-World Relevance
| Dimension | Detail |
|-----------|--------|
| **Domain** | Software Engineering / DevOps / Issue Tracking |
| **Industry use** | Every software company with a bug tracker (GitHub Issues, Jira, Linear, etc.) |
| **Scale** | Large teams process 100–500+ bug reports per week |
| **Cost of wrong triage** | Critical bugs missed → outages; low-priority spamming senior devs → waste |
| **Current solutions** | Manual labels, basic keyword rules, ML classifiers (limited context) |
| **LLM advantage** | Can reason over free-text descriptions, logs, and metadata together |
---
## Users
| User | Need |
|------|------|
| **Developers** | Only receive bugs relevant to their specialization |
| **QA Engineers** | Know which bugs to test first (priority-ordered) |
| **Project Managers** | Accurate sprint planning based on classified backlogs |
| **Engineering Leads** | Automated triage frees team from manual label overhead |
---
## Example Scenario
```
Bug Reported:
Title: "App crashes on iOS 17 when uploading files > 50MB"
Description: "Consistently crashes immediately on upload tap. Blocking
3 enterprise customers."
Logs: "FATAL: EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x0000000000000000
Stack: FileUploadManager.upload(url:size:)"
Environment: "iOS 17.2, iPhone 15 Pro, App v3.2.1"
Agent Must Output:
bug_type: crash ← Task 1
priority: critical ← Task 2
assigned_developer: Alice ← Task 3 (crash specialist)
suggested_action: fix_immediately ← Task 3
```
---
## Real-World Constraints the Agent Must Handle
- **Ambiguous descriptions** — reporters with varying technical skill
- **Incomplete logs** — stack traces cut off or missing
- **Priority inflation** — reporters who label everything as "critical"
- **Routing uncertainty** — cross-cutting bugs (e.g., security + crash)
- **Missing environment info** — agent must infer from context
---
## Tech Stack
| Layer | Technology |
|-------|-----------|
| Environment server | FastAPI + Uvicorn |
| Containerisation | Docker |
| Deployment | Hugging Face Spaces |
| Training | TRL GRPOTrainer + vLLM |
| Base model | Qwen/Qwen3-1.7B |
| Package manager | uv |
| Validation | Pydantic v2 |
| Baseline LLM | OpenAI GPT-4o-mini (via `OPENAI_API_KEY`) |
---
## Repository Layout
```
bug_triage_env/
├── models.py ← Pydantic-typed Action / Observation / State
├── client.py ← HTTP client for training code
├── baseline.py ← OpenAI-backed inference script
├── openenv.yaml ← Manifest
├── pyproject.toml
├── requirements.txt
├── data/
│ └── bugs.json ← 15 diverse real-world bug reports
├── graders/
│ ├── task1_grader.py ← Bug type classification [0.0–1.0]
│ ├── task2_grader.py ← Priority assignment [0.0–1.0]
│ └── task3_grader.py ← Full triage [0.0–1.0]
└── server/
├── environment.py ← OpenEnv Environment ABC implementation
├── app.py ← FastAPI (standard + hackathon endpoints)
└── Dockerfile
```
---
## Hackathon Compliance Checklist
| Requirement | Status |
|-------------|--------|
| HF Space deploys + `/reset` returns 200 | ☐ |
| `openenv.yaml` present | ☐ |
| Typed models (`Action`, `Observation`, `State`) | ☐ |
| `step()` / `reset()` / `state()` implemented | ☐ |
| Dockerfile builds | ☐ |
| `/tasks` — task list + action schema | ☐ |
| `/grader` — score in `[0.0, 1.0]` | ☐ |
| `/baseline` — OpenAI inference, all 3 tasks | ☐ |
| 3+ graded tasks with varying scores | ☐ |
| `baseline.py` runs without error | ☐ |
|