README.md

metadata

pretty_name: SCBench Problems
license: mit
task_categories:
  - text-generation
tags:
  - code
  - benchmark
  - iterative-specification
  - python
  - js
  - cpp
  - rust
  - java
  - scbench
size_categories:
  - n<1K
configs:
  - config_name: python
    data_files:
      - split: test
        path: data/python/test.parquet
  - config_name: js
    data_files:
      - split: test
        path: data/js/test.parquet
  - config_name: cpp
    data_files:
      - split: test
        path: data/cpp/test.parquet
  - config_name: rust
    data_files:
      - split: test
        path: data/rust/test.parquet
  - config_name: java
    data_files:
      - split: test
        path: data/java/test.parquet

SCBench Problems

This dataset contains SlopCodeBench (SCBench) problem checkpoints for evaluating coding agents under iterative specification refinement.

Source repository: https://github.com/SprocketLab/scb-problems.git Source revision: ba1f7fec544dae4ff274d2447d9b65aebfbc5196

Dataset summary

• Language subsets: python, js, cpp, rust, java
• Problems: 36 problems
• Rows: 980 rows
• Columns: problem, Checkpoint number, language, difficulty, tags, instruction, tests

Rows per subset:

• python: 196 rows
• js: 196 rows
• cpp: 196 rows
• rust: 196 rows
• java: 196 rows

Category distribution:

• developer-tools: 8
• web: 7
• data-processing: 6
• cli-tools: 5
• algorithms: 2
• configuration-management: 2
• dsl: 2
• databases: 1
• file-systems: 1
• networking: 1
• simulation: 1

Difficulty distribution:

• Easy: 12
• Hard: 12
• Medium: 12

Using the tests field

Each row includes a tests value containing a complete Bash test runner for that checkpoint. Save it as a file, then run it against a submission directory whose contents implement the entry file named in the instruction field.

Test script examples

Write and run one checkpoint from Python:

from pathlib import Path
import subprocess

from datasets import load_dataset

dataset = load_dataset("gabeorlanski/slopcodebench", "python", split="test")
row = dataset[0]

test_path = Path("test.sh")
test_path.write_text(row["tests"])
test_path.chmod(0o755)

subprocess.run(["bash", str(test_path), "/path/to/submission"], check=True)

Run a saved test script directly:

bash test.sh /path/to/submission

Use a local problems checkout to avoid cloning:

SCB_PROBLEMS_REPO=/path/to/scb-problems bash test.sh /path/to/submission

Keep the temporary workspace for debugging:

SCB_KEEP_WORKDIR=1 bash test.sh /path/to/submission

The runner creates a temporary workspace, copies the submission into it, materializes the relevant checkpoint tests and static assets, installs Python test dependencies in a virtual environment, and invokes pytest. Set SCB_PROBLEMS_REPO to an existing problems checkout to avoid cloning. Set SCB_KEEP_WORKDIR=1 to keep the temporary workspace for debugging.

For cpp, rust, and java subsets, the runner compiles the declared source file with g++, rustc, or javac before running the Python test harness. The js subset expects node to be available.