README.md

---
license: mit
tags:
  - code-generation
  - code-completion
  - benchmark
  - software-engineering
pretty_name: DevBench
---

# DevBench

This dataset packages the **DevBench** code-completion benchmark published by Microsoft, reformatted
into a single `data.jsonl` file with a `category` field identifying the task category of each record.

## Source

- **Official repository:** [microsoft/devbench](https://github.com/microsoft/devbench) (`benchmark/` folder)
- **Retrieved:** 2026-09-17, from the `main` branch of the repository (shallow clone).

## Paper

> Kumarappan, A., Golnari, P. A., Wen, W., Liu, X., Ryan, G., Sun, Y., Fu, S., & Nallipogu, E. (2026).
> **DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models.**
> arXiv:2601.11895. https://arxiv.org/abs/2601.11895

```bibtex
@misc{devbench2026,
  author        = {Kumarappan, Adarsh and Golnari, Pareesa Ameneh and Wen, Wen and Liu, Xiaoyu and Ryan, Gabriel and Sun, Yuting and Fu, Shengyu and Nallipogu, Elsie},
  title         = {{DevBench}: A Realistic, Developer-Informed Benchmark for Code Generation Models},
  year          = {2026},
  eprint        = {2601.11895},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2601.11895}
}
```

## License

**MIT License** — Copyright (c) Microsoft Corporation, as stated in the
[`LICENSE`](https://github.com/microsoft/devbench/blob/main/LICENSE) file of the source repository.
Redistribute in accordance with the MIT License terms (retain the copyright notice).

## File

- `data.jsonl` — 1,800 records, one JSON object per line, UTF-8, each with `language` and `category` fields.

## Structure

DevBench is organized as `benchmark/{language}/{category}/{category}.jsonl`, i.e. **6 programming
languages × 6 task categories × 50 tasks = 1,800 tasks**. All 36 source shards were concatenated,
each record tagged with:

- `category` — the task category, taken from the shard's parent directory name:
  `api_usage`, `code2NL_NL2code`, `code_purpose_understanding`, `low_context`, `pattern_matching`,
  `syntax_completion` (50 records each × 6 languages = 300 records per category).
- `language` — already present on every source record (`python`, `javascript`, `typescript`, `java`,
  `cpp`, `c_sharp`); kept as-is (re-added defensively only if a record were ever missing it, which
  did not occur here).

Each record also retains its original fields: `id`, `testsource`, `prefix` (code visible to the
model before the cursor), `golden_completion` (reference answer), `suffix` (code visible after the
cursor), and `assertions` (hidden unit-test-style checks used to grade a completion, not shown to
the model under evaluation).

| `category` | Records | Description |
|---|---:|---|
| `api_usage` | 300 | Completions exercising a specific library/API call |
| `code2NL_NL2code` | 300 | Code↔natural-language translation tasks |
| `code_purpose_understanding` | 300 | Completions requiring understanding of surrounding code intent |
| `low_context` | 300 | Completions with minimal surrounding context |
| `pattern_matching` | 300 | Completions following a repeated code pattern |
| `syntax_completion` | 300 | Syntax-level fill-in-the-middle completions |
| **Total** | **1,800** | |

## Known issues / decisions made while preparing this package

1. Only the `benchmark/` folder (the actual task set) was packaged, per the task scope. The
   repository also ships `completions/` (pre-generated model outputs from 9 models),
   `judge_completions/` (LLM-judge scores), `prompts/`, `evaluation/`, and `analysis/` — these are
   evaluation artifacts/tooling, not the benchmark data itself, and were left out of `data.jsonl`.
2. Each shard's companion `*_formatted.txt` file (a human-readable rendering of the same JSONL data,
   used for manual inspection) was skipped as redundant with the `.jsonl` source.
3. Record counts were verified as exactly 50 per shard × 36 shards = 1,800 before and after
   processing; no records were dropped or deduplicated.
4. Serialized with PowerShell's `ConvertTo-Json` (no Python available in the preparation
   environment); non-ASCII characters, if any, are escaped as `\uXXXX`, which is valid JSON and was
   validated line-by-line.