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language:
- zh
- en
license: apache-2.0
task_categories:
- text-generation
- question-answering
tags:
- benchmark
- code
- sap
- abap
- s4hana
- enterprise-software
- llm-evaluation
pretty_name: ABAP-Bench
size_categories:
- n<1K
---
# ABAP-Bench
A comprehensive benchmark for evaluating Large Language Model understanding of SAP ABAP programming, S/4HANA modernization, and enterprise software engineering.
## Overview
| Property | Value |
|----------|-------|
| Tasks | 60 |
| Dimensions | 9 |
| Max raw score | 1200 (normalized to 100) |
| Scoring layers | 4 (Rubric + Quality + Semantic + LLM-as-Judge) |
| Language | Chinese (primary), English (partial), ABAP |
| License | Apache-2.0 |
## Dimensions
1. **Code Migration** (6 tasks) — ECC → S/4HANA API replacement
2. **Defect Discovery** (6 tasks) — Finding hidden bugs in ABAP code
3. **Code Rewriting** (6 tasks) — Modernizing legacy ABAP
4. **China Compliance** (6 tasks) — Golden Tax, PIPL, payroll regulations
5. **Migration Risk** (6 tasks) — Change impact and dependency analysis
6. **Security & Auth** (6 tasks) — Authorization, injection, transport security
7. **S/4HANA Architecture** (6 tasks) — ACDOCA, CDS views, FI-CO integration
8. **Performance Engineering** (6 tasks) — SELECT optimization, HANA features
9. **Modern Ecosystem** (12 tasks) — Clean Core, testing, Fiori, workflow, IDoc
## Usage
```python
import json
# Load tasks
tasks = []
with open("data/tasks.jsonl") as f:
for line in f:
tasks.append(json.loads(line))
# Each task has: task_id, title, dimension, max_score, prompt_template
print(tasks[0]["prompt_template"])
```
## Evaluation
```bash
pip install -e .
python -m src.evaluate_v2 --task T01 --response "your model output" --breakdown
```
## Citation
```bibtex
@misc{abapbench2026,
title={ABAP-Bench: A Benchmark for Evaluating LLM Understanding of SAP ABAP and S/4HANA Modernization},
author={ABAP-Bench Contributors},
year={2026},
version={4.0},
howpublished={\url{https://github.com/abap-bench/abap-bench}},
}
```
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