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