README.md

---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - benchmark
  - agents
  - llm-agents
  - test-driven-development
  - specification
  - mutation-testing
pretty_name: SpecSuite-Core
arxiv: 2603.08806
size_categories:
  - n<1K
configs:
  - config_name: specs
    data_files:
      - split: train
        path: data/specs/train.jsonl
  - config_name: mutations
    data_files:
      - split: train
        path: data/mutations/train.jsonl
  - config_name: results
    data_files:
      - split: train
        path: data/results/train.jsonl
---

# SpecSuite-Core

**SpecSuite-Core** is a benchmark suite of 4 deeply-specified AI agent behavioral specifications, designed to evaluate the **TDAD (Test-Driven Agent Definition)** methodology described in our paper.

Paper: [Test-Driven Agent Definition (TDAD)](https://arxiv.org/abs/2603.08806)
Code: [GitHub](https://github.com/f-labs-io/tdad-paper-code)

## Overview

Each specification defines a complete agent behavior contract including:
- **Tools** with typed input/output schemas and failure modes
- **Policies** with priorities and enforcement levels
- **Decision trees** defining the agent's control flow
- **Response contracts** with required JSON fields
- **Mutation intents** for robustness testing
- **Spec evolution** (v1 → v2) for backward compatibility testing

## Specifications

| Spec | Domain | v1 Tools | v1 Policies | v2 Change |
|------|--------|----------|-------------|-----------|
| **SupportOps** | Customer support (cancel, address, billing) | 7 | 4 | Abuse detection |
| **DataInsights** | SQL analytics & reporting | 3 | 4 | Cost-aware queries |
| **IncidentRunbook** | Incident response & escalation | 6 | 4 | Customer impact tracking |
| **ExpenseGuard** | Expense approval & reimbursement | 6 | 5 | Manager approval gate |

## Configs

### `specs` — Agent Specifications (8 rows)

Each row is one spec version (4 specs × 2 versions). The `spec_yaml` field contains the full YAML specification.

```python
from datasets import load_dataset
specs = load_dataset("f-labs-io/SpecSuite-Core", "specs")

# Get SupportOps v1
spec = specs["train"].filter(lambda x: x["spec_id"] == "supportops_v1")[0]
print(spec["title"])        # "SupportOps Agent"
print(spec["tool_names"])   # ["verify_identity", "get_account", ...]
print(spec["spec_yaml"])    # Full YAML specification
```

### `mutations` — Mutation Intents (27 rows)

Each row is a mutation intent: a description of a plausible behavioral failure that the test suite should detect.

```python
mutations = load_dataset("f-labs-io/SpecSuite-Core", "mutations")

# All critical mutations
critical = mutations["train"].filter(lambda x: x["severity"] == "critical")
for m in critical:
    print(f"{m['spec_lineage']}/{m['mutation_id']}: {m['intent'][:80]}...")
```

Categories: `policy_violation`, `process_violation`, `business_logic_violation`, `grounding_violation`, `escalation_violation`, `safety_violation`, `quality_regression`, `compliance_violation`, `robustness_violation`, `decision_violation`, `tooling_violation`

### `results` — Pipeline Run Results (24 rows)

Each row is one end-to-end TDAD pipeline run with metrics across all 4 evaluation dimensions.

```python
results = load_dataset("f-labs-io/SpecSuite-Core", "results")

for r in results["train"]:
    print(f"{r['spec']}/{r['version']}: VPR={r['vpr_percent']}% HPR={r['hpr_percent']}% MS={r['mutation_score']}% ${r['total_cost_usd']:.2f}")
```

## Metrics

| Metric | Full Name | What It Measures |
|--------|-----------|-----------------|
| **VPR** | Visible Pass Rate | Compilation success (tests seen during prompt optimization) |
| **HPR** | Hidden Pass Rate | Generalization (held-out tests never seen during compilation) |
| **MS** | Mutation Score | Robustness (% of seeded behavioral faults detected by tests) |
| **SURS** | Spec Update Regression Score | Backward compatibility (v1 tests passing on v2 prompt) |

## Full Pipeline

The specifications in this dataset are designed to be used with the TDAD pipeline:

1. **TestSmith** generates executable tests from the spec
2. **PromptSmith** iteratively compiles a system prompt until tests pass
3. **MutationSmith** generates behavioral mutations to measure test quality
4. Tests and mutations measure the agent across VPR, HPR, MS, and SURS

See the [GitHub repo](https://github.com/f-labs-io/tdad-paper-code) for the full executable pipeline with Docker support.

## Citation

```bibtex
@article{rehan2026tdad,
  title={Test-Driven Agent Definition: A Specification-First Framework for LLM Agent Development},
  author={Rehan, Tzafrir},
  year={2026}
}
```

## License

Apache 2.0