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Datasets:
cezar-sas
/
code-theseus

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
software-engineering
requirements-engineering
software-architecture
reverse-engineering
c4-model
code-generation
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
code-theseus
File size: 4,364 Bytes
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---
license: cc-by-4.0
pretty_name: code-theseus
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
tags:
- software-engineering
- requirements-engineering
- software-architecture
- reverse-engineering
- c4-model
- code-generation
- coding-agents
- llm
- synthetic
configs:
- config_name: documents
  data_files: documents/train.parquet
  default: true
- config_name: repos
  data_files: repos/train.parquet
---

<div align="center">

# 🚢 code-theseus

**The design behind 10,000+ open-source repositories: intent, requirements
and architecture, aligned with the code, ready for training coding agents.**

| 📦 **10,813** | 📄 **21,623** | 🌐 **4** | 🤖 **2** |
|:---:|:---:|:---:|:---:|
| repositories | document sets | languages: Java, Python, C/C++, C# | generator LLMs |



</div>

## 🧭 Why this dataset

Coding agents increasingly design before they code, but almost no data
connects code to the requirements and architecture behind it: real projects
rarely keep these documents, and when they do they are outdated or private.
code-theseus recovers them from the source code, at scale.

## 📦 What's inside

Every repository comes with three aligned documents, generated from its
dependency graph and key project files:

| | Document | What it captures |
|---|---|---|
| 🎯 | **`intent`** | The "initial prompt": mission, problem, capabilities, user workflows, quality expectations |
| 📋 | **`requirements`** | `REQ-FUNC-*` / `REQ-NFR-*` items with statement, rationale, acceptance criteria, verification method and priority |
| 🏗️ | **`architecture`** | A C4 design (context, container, component) with Mermaid C4 diagrams |

…plus the **package-level dependency graph** each document was generated
from, and a link to the exact commit it describes. No source code is
redistributed: check out `url` at `commit_sha` to recover it.

## 🔁 Does it carry the design?

In the paper's Ship of Theseus test, a coding agent rebuilt
[Commit0](https://github.com/commit-0/commit0) libraries from these
documents alone. With requirements and architecture, it built a module
structure **significantly closer to the original** than with intent alone
(module F1 0.54 → 0.63), at the same test pass rate.

## 🏢 Use it for

- **Fine-tuning** models that write specs from code (graph → intent,
  requirements, architecture) or code from specs.
- **Spec-driven coding agents.** Realistic requirements and architecture
  briefs as agent inputs.
- **Evaluating** the intermediate steps of multi-agent development, not just
  the final code.
- **Research** on requirements engineering, software architecture recovery
  and traceability.

## 🚀 Quick start

```python
from datasets import load_dataset

docs = load_dataset("anonymous/code-theseus", "documents", split="train")
repos = load_dataset("anonymous/code-theseus", "repos", split="train")
graphs = dict(zip(repos["repo_id"], repos["arcan_graph"]))
graph = graphs[docs[0]["repo_id"]]  # input graph for the first document
```

## 🗂️ Configs

### `documents` (21,623 rows): one row per (repository, generator)

| column | description |
|---|---|
| `repo_id` | `owner\|repo`; join key to `repos` |
| `generator` | `gemma-4-31b-it` / `qwen3.5-122b-a10b` |
| `checkpoint` | Hugging Face checkpoint served with vLLM |
| `intent`, `requirements`, `architecture` | generated Markdown; null if missing, truncated or empty (56 docs) |
| `reasoning_*` | the generator's thinking trace (Qwen only, null otherwise) |

### `repos` (10,813 rows): one row per repository

| column | description |
|---|---|
| `repo_id` | `owner\|repo` |
| `branch` | `main` (GitHub corpus) or `commit0` ([Commit0](https://github.com/commit-0/commit0) libraries) |
| `url` | GitHub URL |
| `commit_sha` | analysed commit; documents describe the repository at this commit |
| `language`, `description`, `stars` | GitHub metadata at collection time |
| `arcan_graph` | package-level dependency graph from [Arcan](https://www.arcan.tech/): `nodes` (`vertex_id`, `name`, `component_type`, `fan_in`, `fan_out`, `lines_of_code`, `pagerank`) and weighted `edges` (`source`/`target` are `vertex_id`s) |

## 📜 License

[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). This covers the
generated documents and dependency graphs. The original repositories keep
their own licenses.