| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - benchmark |
| - code |
| - evaluation |
| - debugging |
| size_categories: |
| - n<1K |
| --- |
| |
| # Code Stress Benchmark |
|
|
| 50-task evaluation benchmark for code-focused LLMs. Built to test whether a fine-tune holds up across realistic workflow scenarios — not just isolated bug-fix puzzles. |
|
|
| ## Categories |
|
|
| | Category | Tasks | What it tests | |
| |---|---|---| |
| | easy_bug | 5 | Common Python/JS pitfalls | |
| | tricky_bug | 5 | Race conditions, default args, scoping | |
| | medium_script | 10 | Multi-step Python/JS/SQL/Bash | |
| | hard_algorithm | 10 | LRU cache, DP, graph traversal, LCA | |
| | edge_cases | 5 | Boundary conditions, error handling | |
| | architecture_review | 5 | Code review on small services | |
| | engineering_judgment | 5 | "Should I do X or Y?" prompts | |
| | design_reasoning | 5 | Open-ended system design | |
|
|
| ## Format |
|
|
| ```json |
| { |
| "id": "bug_easy_01", |
| "category": "easy_bug", |
| "language": "python", |
| "prompt": "Find and fix the bug:\n\n```python\ndef average(nums):\n return sum(nums) / len(nums)\n\nprint(average([]))\n```" |
| } |
| ``` |
|
|
| ## Use |
|
|
| Designed for A/B comparison: run the same task through base and tuned models, then measure: |
| - **length ratio** (lora / base) — does the tune produce more focused answers? |
| - **lazy regression** — does the tune drop code blocks the base produced? |
| - **correctness** — manual review |
|
|
| Used to evaluate [NecroMOnk/Residual](https://huggingface.co/NecroMOnk/Residual) and [NecroMOnk/Tersa](https://huggingface.co/NecroMOnk/Tersa). |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("NecroMOnk/code-stress-bench") |
| ``` |
|
|
