Datasets:

rohhaiil
/

SysMLv2_Repair_with_SLMs

@@ -1,75 +1,80 @@
----
-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
----
-# 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.
-## Splits
-- **train**: 5,451 examples
-- **validation**: 1,184 examples
-- **test**: 1,145 examples
-## 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 total 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. The dataset was 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}
-}

+---
+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: train
+    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.
+## Splits
+- **train**: 5,451 examples
+- **validation**: 1,184 examples
+- **test**: 1,145 examples
+## 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 total 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. The dataset was 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}
+}