README.md

---
pretty_name: JumpForge Agentic SE 3K
language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
- question-answering
tags:
- software-engineering
- coding-agents
- agent-behavior
- tool-use
- debugging
- verification
- repository-understanding
- safety
- synthetic
- jumplander
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.jsonl
  - split: validation
    path: data/validation.jsonl
  - split: test
    path: data/test.jsonl
---

# JumpForge-Agentic-SE-3K

**JumpForge-Agentic-SE-3K** is a structured synthetic dataset for training and evaluating
AI software-engineering agents. Its primary target is **agent behavior across the software
engineering lifecycle**, not raw code generation or memorization of programming-language syntax.

The dataset teaches an agent to:

- understand intent and ambiguity before acting;
- explore repositories and trace system behavior;
- decompose work into reversible steps;
- select tools based on information value;
- debug causally instead of patching symptoms;
- preserve contracts, security, and operational safety;
- verify changes with failure-focused tests;
- recover from failed actions;
- calibrate uncertainty and escalate responsibly;
- communicate evidence, risk, and remaining unknowns;
- maintain state across long-horizon engineering tasks.

## Dataset size

| Split | Records | Archetype groups |
|---|---:|---:|
| Train | 2400 | 96 |
| Validation | 300 | 12 |
| Test | 300 | 12 |
| **Total** | **3,000** | **120** |

Each archetype contains 25 independently parameterized scenarios. Archetypes are kept entirely
inside one split to reduce behavioral-template leakage between train, validation, and test.

## Behavioral taxonomy

The dataset has 12 top-level families and 120 archetypes:

1. Problem understanding
2. Repository exploration
3. Planning and decomposition
4. Tool selection
5. Debugging behavior
6. Safe modification
7. Verification and testing
8. Error recovery
9. Security-aware engineering
10. Uncertainty and escalation
11. Communication and reporting
12. Long-horizon execution

See `docs/TAXONOMY.md` and `configs/taxonomy.json`.

## Record design

Each record includes:

- user request and system context;
- repository structure and relevant files;
- evidence, logs, and a focused failing test;
- hidden ground truth for evaluation;
- ideal agent assessment, assumptions, plan, and tool trace;
- observations, decision, implementation strategy, verification, and stopping criteria;
- bad-behavior counterexamples;
- evaluation rubric with must-do and must-not-do conditions;
- chat-style `messages` for SFT-compatible workflows;
- machine-validation metadata and hashes.

## Example access

```python
from datasets import load_dataset

dataset = load_dataset("jumplander/JumpForge-Agentic-SE-3K")
print(dataset["train"][0]["task"])
```

Local JSONL:

```python
import json

with open("data/train.jsonl", encoding="utf-8") as f:
    first = json.loads(next(f))

print(first["ideal_agent_behavior"]["plan"])
```

## Deduplication and leakage controls

The release includes machine-readable reports under `reports/`.

Current generated-release audit:

- exact duplicate IDs: **0**
- exact duplicate user requests: **0**
- normalized duplicate user requests: **0**
- duplicate final responses: **0**
- duplicate structural fingerprints: **0**
- archetype split leakage: **0**
- maximum TF-IDF nearest-neighbor similarity: **0.7350**

The lexical check is a heuristic. It should not be interpreted as proof that all records are
semantically independent. Before high-stakes model training or research publication, perform
expert review and embedding/model-assisted semantic audits.

## Important limitations

- The dataset is synthetic.
- Machine validation checks structure, consistency constraints, uniqueness, and split policy.
- The included heuristic quality score is not human quality certification.
- Code paths and evidence are realistic abstractions, not executable full repositories.
- Tool traces describe expected agent decisions; they are not logs from tools actually executed.
- Some phrasing and structural regularity remains because the dataset is generated from a controlled taxonomy.

## Recommended uses

- supervised fine-tuning for agent planning and communication;
- behavior scoring and evaluator development;
- tool-selection and stopping-criteria research;
- repository-reasoning curriculum design;
- preference-data generation using the included bad behaviors;
- benchmark prototyping for safe software-engineering agents.

## Not recommended

- measuring real-world patch correctness without executable repositories;
- claiming production-grade coding performance from this dataset alone;
- security certification;
- direct autonomous execution on sensitive systems.

## License

CC BY 4.0. See `LICENSE`.

## Attribution

Created by **JumpLander** for research on agentic software engineering.