Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: Developed Cross Domain Control Property Graph (CDCPG). And used GraphGL to evaluate the proposed graph dataset.
strengths_and_weaknesses: ## Strengths:
### 1. The motivation of this paper is sound: "There are projects which their source codes are not available and the owners did not make the projects open-source".

## Weaknesses:
### 1. The writing of this paper is not good enough. There are **many grammar mistakes or typos** in the paper. 
For example, in the Abstract: 'in the process find' should be 'in the process of finding', in the Introduction Section: 'import role' should be 'important role', and in the Proposed Method Section, 'performance compare with', should be 'performance compared with. 'And most of the nouns lack articles.

### 2. Did not write clearly how to generate the CDCPG graph. Figure 1 is not referred to in the text, and no explanation of the process of generation.
Since it is the most crucial part of this paper, it should be clearly explained in the text.

suggested_changes: 1. Should explain how to generate CDCPG from six graph datasets.

2. Should explain how to evaluate the quality of the CDCPG.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This work generate a custom dataset with graphs to evalute the model performance. The state-of-the-art method is the contrastive learning for vulnerability detection task. However, the relationship between vulnerability detection and GraphCL is not well explained.
strengths_and_weaknesses: Strength:
•	Authors proposed a new graph dataset to detect vulnerabilities using GraphCL, which adopts graph augmentation methods and evaluates the result based on different loss functions.

Weakness:
•	The relationship between vulnerability detection and GraphCL is not well explained.

•	The task is not clear, i.e., is framework proposed for training a better and robustness model?

•	The contribution is not well stated.

suggested_changes: •	What does the meaning of the code-level and binarylevel CPG graphs?

•	The motivation and the task need to be polished.
review_title: null

final_score: rating: High Potential (HP): a submission which meets the reviewing criteria and has potential to make an impact on the field
review_content: summary_of_contributions: The authors evaluate the performance of GraphCL, a state-of-the-art Contrastive Learning (CL) method for Self-Supervised Learning (SSL) in vulnerability detection. They introduce a custom dataset featuring a new graph structure called Cross Domain Control Property Graph (CDCPG), which combines code-level and binary-level CPG graphs for improved detection.
strengths_and_weaknesses: Strengths:

1. The creation of a novel CDCPG dataset offers a unique and more comprehensive representation for vulnerability detection tasks.

2. This thorough investigation helps identify the best combination to mitigate class imbalance and improve model performance.

3. The study contributes valuable insights into SSL methods for addressing the challenge of limited labeled data in the field of vulnerability detection.
suggested_changes: None
review_title: null

Meta-review: metareview: Good paper with a clear motivation. It could be better if suggestions from reviewers could be incorporated into the manuscript. Especially, the writing could be further improved.
confidence: 4: The area chair is confident but not absolutely certain
Decision: Invite to revise