Datasets:
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pretty_name: Anonymized Iterative Code Benchmark Problems
license: mit
task_categories:
- text-generation
tags:
- code
- benchmark
- iterative-specification
- python
- js
- cpp
- rust
- java
- anonymized
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
---
# Anonymized Iterative Code Benchmark Problems
This anonymized dataset contains multi-checkpoint coding tasks for
evaluating agents under iterative specification refinement. Problem
identifiers, source repository identifiers, and author fields are
intentionally omitted.
## 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:
```python
from pathlib import Path
import subprocess
from datasets import load_dataset
dataset = load_dataset("anon/dsn26", "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
bash test.sh /path/to/submission
```
Use a local problems checkout to avoid cloning:
```bash
BENCH_PROBLEMS_REPO=/path/to/problems bash test.sh /path/to/submission
```
Keep the temporary workspace for debugging:
```bash
BENCH_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 `BENCH_PROBLEMS_REPO` to an existing problems checkout to
avoid cloning. Set `BENCH_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.
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