hqburke commited on
Commit
db312ad
·
1 Parent(s): 65d392a

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +27 -16
README.md CHANGED
@@ -18,31 +18,42 @@ dataset_info:
18
  download_size: 33528231
19
  dataset_size: 156327244
20
  ---
21
- # Dataset Card for "AGabs_finetuning"
 
 
22
 
23
- Dataset is imported from CodeXGLUE and pre-processed using their script.
24
- Where to find in Semeru:
25
  The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru
26
 
27
- CodeXGLUE -- Defect Detection
28
- Task Definition
 
 
 
29
  Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
30
 
31
- Dataset
32
- The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test.
 
 
 
33
 
34
- Data Format
35
  Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present
36
 
37
  For each file, each line in the uncompressed file represents one function. One row is illustrated below.
38
 
39
- func: the source code
40
- target: 0 or 1 (vulnerability or not)
41
- idx: the index of example
42
- Data Statistics
 
 
43
  Data statistics of the dataset are shown in the below table:
44
 
45
- #Examples
46
- Train 126,477
47
- Dev 15,809
48
- Test 15,810
 
 
 
 
 
18
  download_size: 33528231
19
  dataset_size: 156327244
20
  ---
21
+ ### Dataset is imported from CodeXGLUE and pre-processed using their script.
22
+
23
+ # Where to find in Semeru:
24
 
 
 
25
  The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-code/Defect-detection in Semeru
26
 
27
+
28
+ # CodeXGLUE -- Defect Detection
29
+
30
+ ## [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#task-definition)Task Definition
31
+
32
  Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
33
 
34
+ ### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#dataset)Dataset
35
+
36
+ The dataset we use comes from the paper [_Devign_: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). We combine all projects and split 80%/10%/10% for training/dev/test.
37
+
38
+ ### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#data-format)Data Format
39
 
 
40
  Three pre-processed .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl are present
41
 
42
  For each file, each line in the uncompressed file represents one function. One row is illustrated below.
43
 
44
+ - **func:** the source code
45
+ - **target:** 0 or 1 (vulnerability or not)
46
+ - **idx:** the index of example
47
+
48
+ ### [](https://huggingface.co/datasets/semeru/code-code-DefectDetection#data-statistics)Data Statistics
49
+
50
  Data statistics of the dataset are shown in the below table:
51
 
52
+
53
+
54
+ | | Description |
55
+ | ----------- | ----------- |
56
+ | Train| 126,477|
57
+ | Dev| 15,809|
58
+ |Test|15,810
59
+