rohhaiil commited on
Commit
f180cbd
·
verified ·
1 Parent(s): d63d11f

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +76 -87
README.md CHANGED
@@ -1,87 +1,76 @@
1
- ---
2
- license: cc-by-4.0
3
- configs:
4
- - config_name: default
5
- data_files:
6
- - split: train
7
- path: train.jsonl
8
- - split: validation
9
- path: validation.jsonl
10
- - split: test
11
- path: test.jsonl
12
- ---
13
-
14
- # SysMLv2 Repair with SLMs
15
-
16
- 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.
17
-
18
- ## Splits
19
-
20
- - **train**: 5,451 examples
21
- - **validation**: 1,184 examples
22
- - **test**: 1,145 examples
23
-
24
- ## Task
25
-
26
- 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.
27
-
28
- ## Fields
29
-
30
- - `id`: Unique identifier for each dataset instance.
31
-
32
- - `source_id`: Identifier of the original (clean) code example from which this instance was derived.
33
-
34
- - `mutation_category`: High-level category of the applied mutation:
35
- - `domain`: domain-specific semantic changes
36
- - `syntax`: syntactic errors
37
- - `none`: no mutation applied
38
-
39
- - `mutation_type`: Specific mutation operator used to generate the erroneous code.
40
-
41
- - `bad_code`: SysML v2 code potentially containing injected errors.
42
-
43
- - `good_code`: Correct version of the code.
44
-
45
- - `diff_patch`: Unified diff representing the transformation from `bad_code` to `good_code`.
46
-
47
- - `base_prompt`: Base prompt template used for fine-tuning; contains `bad_code` and compiler error in case of `syntax` mutations.
48
-
49
- - `prompt`: Prompt containing additional context (e.g., relevant domain rules), for `domain` and `none` mutations, where no compiler error occurs.
50
-
51
- - `code_response`: Repair in full code form.
52
-
53
- - `patch_response`: Repair in diff/patch format.
54
-
55
- - `length`: Total number of tokens in the full training sequence (prompt + response).
56
-
57
- ## Data Creation
58
-
59
- 1. A seed set of 256 examples was created from a combination of public and author-generated SysML v2 code.
60
- 2. Synthetic errors were introduced via:
61
- - **Syntactic mutations** (5,497 instances)
62
- - **Domain/semantic mutations** based on violations of rules defined in a knowledge graph (1,402 instances)
63
- 3. An equal number of correct (unmutated) examples were included to support classification of correct vs. erroneous code, resulting in 8,301 total instances.
64
- 4. Additional context was generated for each example:
65
- - Compiler error messages for syntactic errors
66
- - Relevant domain rules for semantic cases
67
- 5. Target outputs were derived, including corrected code and corresponding diff patches.
68
- 6. The dataset was split into train/validation/test sets using a 70/15/15 ratio.
69
-
70
- ## License
71
-
72
- This dataset is released under CC BY 4.0. Attribution to the original authors is required.
73
-
74
- ## Citation
75
-
76
- GitHub Repository: [SysMLv2 Repair with KG-SLMs](https://github.com/rohailamalik/SysMLv2-repair-with-KG-SLMs)
77
-
78
- ```bibtex
79
- @inproceedings{alshami2026sysml,
80
- title={Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs},
81
- author={Al-Shami, Haitham and Malik, Rohail and Ala-Laurinaho, Riku and Veps{\"a}l{\"a}inen, Jari and Viitala, Raine},
82
- booktitle={Proceedings of the 36th INCOSE International Symposium},
83
- year={2026},
84
- address={Yokohama, Japan},
85
- month={June},
86
- date={16}
87
- }
 
1
+ ---
2
+ license: cc-by-4.0
3
+ configs:
4
+ - config_name: default
5
+ data_files:
6
+ - split: train
7
+ path: train.jsonl
8
+ - split: validation
9
+ path: validation.jsonl
10
+ - split: test
11
+ path: test.jsonl
12
+ ---
13
+
14
+ # SysMLv2 Repair with SLMs
15
+
16
+ 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.
17
+
18
+ ## Splits
19
+
20
+ - **train**: 5,451 examples
21
+ - **validation**: 1,184 examples
22
+ - **test**: 1,145 examples
23
+
24
+ ## Task
25
+
26
+ 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.
27
+
28
+ ## Fields
29
+
30
+ - `id`: Unique identifier for each dataset instance.
31
+ - `source_id`: Identifier of the original (clean) code example from which this instance was derived.
32
+ - `mutation_category`: High-level category of the applied mutation:
33
+ - `domain`: domain-specific semantic changes
34
+ - `syntax`: syntactic errors
35
+ - `none`: no mutation applied
36
+ - `mutation_type`: Specific mutation operator used to generate the erroneous code.
37
+ - `bad_code`: SysML v2 code potentially containing injected errors.
38
+ - `good_code`: Correct version of the code.
39
+ - `diff_patch`: Unified diff representing the transformation from `bad_code` to `good_code`.
40
+ - `base_prompt`: Base prompt template used for fine-tuning; contains `bad_code` and compiler error in case of `syntax` mutations.
41
+ - `prompt`: Prompt containing additional context (e.g., relevant domain rules), for `domain` and `none` mutations, where no compiler error occurs.
42
+ - `code_response`: Repair in full code form.
43
+ - `patch_response`: Repair in diff/patch format.
44
+ - `length`: Total number of tokens in the full training sequence (prompt + response).
45
+
46
+ ## Data Creation
47
+
48
+ 1. A seed set of 256 examples was created from a combination of public and author-generated SysML v2 code.
49
+ 2. Synthetic errors were introduced via:
50
+ - **Syntactic mutations** (5,497 instances)
51
+ - **Domain/semantic mutations** based on violations of rules defined in a knowledge graph (1,402 instances)
52
+ 3. An equal number of correct (unmutated) examples were included to support classification of correct vs. erroneous code, resulting in 8,301 total instances.
53
+ 4. Additional context was generated for each example:
54
+ - Compiler error messages for syntactic errors
55
+ - Relevant domain rules for semantic cases
56
+ 5. Target outputs were derived, including corrected code and corresponding diff patches.
57
+ 6. The dataset was split into train/validation/test sets using a 70/15/15 ratio.
58
+
59
+ ## License
60
+
61
+ This dataset is released under CC BY 4.0. Attribution to the original authors is required.
62
+
63
+ ## Citation
64
+
65
+ GitHub Repository: [SysMLv2 Repair with KG-SLMs](https://github.com/rohailamalik/SysMLv2-repair-with-KG-SLMs)
66
+
67
+ ```bibtex
68
+ @inproceedings{alshami2026sysml,
69
+ title={Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs},
70
+ author={Al-Shami, Haitham and Malik, Rohail and Ala-Laurinaho, Riku and Veps{\"a}l{\"a}inen, Jari and Viitala, Raine},
71
+ booktitle={Proceedings of the 36th INCOSE International Symposium},
72
+ year={2026},
73
+ address={Yokohama, Japan},
74
+ month={June},
75
+ date={16}
76
+ }