| --- |
| license: cc-by-4.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train_dataset.jsonl |
| - split: validation |
| path: eval_dataset.jsonl |
| - split: test |
| path: test_dataset.jsonl |
|
|
| - config_name: full |
| data_files: |
| - split: full |
| path: compiled_dataset.jsonl |
| --- |
| |
| # SysMLv2 Repair with SLMs |
|
|
| Dataset used in "Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs", presented at INCOSE International Symposium 2026. |
|
|
| ## Dataset Structure |
|
|
| This dataset provides two configurations: |
|
|
| - **default**: Contains train/validation/test splits used for fine-tuning small models. Samples exceeding 2048 tokens have been removed. |
| - **full**: Contains complete dataset |
|
|
| ## Task |
|
|
| Given SysML v2 code and relevant context (compiler error, or relevant domain rules), identify and fix potential faults in it. The output is either the corrected code or a diff patch for correcting. |
|
|
| ## Fields |
|
|
| - `id`: Unique identifier for each dataset instance. |
| - `source_id`: Identifier of the original (clean) code example from which this instance was derived. |
| - `mutation_category`: High-level category of the applied mutation: |
| - `domain`: domain-specific semantic changes |
| - `syntax`: syntactic errors |
| - `none`: no mutation applied |
| - `mutation_type`: Specific mutation operator used to generate the erroneous code. |
| - `bad_code`: SysML v2 code potentially containing injected errors. |
| - `good_code`: Correct version of the code. |
| - `diff_patch`: Unified diff representing the transformation from `bad_code` to `good_code`. |
| - `base_prompt`: Base prompt template used for fine-tuning; contains `bad_code` and compiler error in case of `syntax` mutations. |
| - `prompt`: Prompt containing additional context (e.g., relevant domain rules), for `domain` and `none` mutations, where no compiler error occurs. |
| - `code_response`: Repair in full code form. |
| - `patch_response`: Repair in diff/patch format. |
| - `length`: Total number of tokens in the full training sequence (prompt + response). |
|
|
| ## Data Creation |
|
|
| 1. A seed set of 256 examples was created from a combination of public and author-generated SysML v2 code. |
| 2. Synthetic errors were introduced via: |
| - **Syntactic mutations** (5,497 instances) |
| - **Domain/semantic mutations** based on violations of rules defined in a knowledge graph (1,402 instances) |
| 3. An equal number of correct (unmutated) examples were included to support classification of correct vs. erroneous code, resulting in 8,301 instances. |
| 4. Additional context was generated for each example: |
| - Compiler error messages for syntactic errors |
| - Relevant domain rules for semantic cases |
| 5. Target outputs were derived, including corrected code and corresponding diff patches. |
| 6. Entries longer than 2048 tokens were dropped, and remaining (7,780 instances) were split into train/validation/test sets using a 70/15/15 ratio. |
|
|
| ## License |
|
|
| This dataset is released under CC BY 4.0. Attribution to the original authors is required. |
|
|
| ## Citation |
|
|
| GitHub Repository: [SysMLv2 Repair with KG-SLMs](https://github.com/rohailamalik/SysMLv2-repair-with-KG-SLMs) |
| ```bibtex |
| @inproceedings{alshami2026sysml, |
| title={Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs}, |
| author={Al-Shami, Haitham and Malik, Rohail and Ala-Laurinaho, Riku and Veps{\"a}l{\"a}inen, Jari and Viitala, Raine}, |
| booktitle={Proceedings of the 36th INCOSE International Symposium}, |
| year={2026}, |
| address={Yokohama, Japan}, |
| month={June}, |
| date={16} |
| } |
| ``` |
