CLEP: A Novel Contrastive Learning Method for Evolutionary Reentrancy Vulnerability Detection
Authors: Jie Chen, Liangmin Wang, Huijuan Zhu, Victor S. Sheng
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that CLEP not only outperforms state-of-the-art baselines in version-specific scenarios but also shows promising performance in cross-version evolution scenarios. |
| Researcher Affiliation | Academia | 1School of Cyber Science and Engineering, Southeast University 2School of Computer Science and Communication Engineering, Jiangsu University 3Department of Computer Science, Texas Tech University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Version-Aware Contrastive Sampler |
| Open Source Code | No | The paper does not provide any statement regarding the release of source code, nor does it include a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | Datasets. 1) Smart Bug Wild (Durieux et al. 2020) is a widely recognized smart contract dataset. [...] 2) Re Tran Study (Zheng et al. 2023) is a recently cross-version smart contract dataset, which contains 139, 424 contracts. |
| Dataset Splits | Yes | Following Peculiar (Wu et al. 2021) and Re Vul DL (Zhang et al. 2022), we split the datasets into training (20%), validation (10%), and testing sets (70%). |
| Hardware Specification | Yes | All experiments were conducted with two Intel Xeon 6148 CPUs, two 3090Ti GPUs, and 512GB RAM. |
| Software Dependencies | No | The paper does not explicitly state specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used in the implementation. |
| Experiment Setup | Yes | The learning rate l tuned amongst {1e 6, 1e 5, 2e 5, 5e 5}, the epoch ep is searched in {4, 6, 8, 10, 15}, code length c in {256, 512}, the batch size bs in {24, 32, 64, 128} and data flow length d in {64, 128}. The performance of all neural network models, unless specifically stated, are reported on the following settings: for VSFT, we set l to 1e 5, ep to 5, bs to 24, c to 256, and d to 64; for CVFT, l was set to 1e 6, ep to 10, bs to 32, c to 256, and d to 64; and for SFT, l, ep, and bs were set to 1e 5, 10, and 32 respectively in the refinement phase, and to 2e 5, 4, and 48 in the fine-tuning phase. |