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Leopard: identifying vulnerable code for vulnerability assessment through program metrics.

, , , , , , and . ICSE, page 60-71. IEEE / ACM, (2019)

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ContrastRepair: Enhancing Conversation-Based Automated Program Repair via Contrastive Test Case Pairs., , , , , and . CoRR, (2024)Leopard: identifying vulnerable code for vulnerability assessment through program metrics., , , , , , and . ICSE, page 60-71. IEEE / ACM, (2019)Marvel: a generic, scalable and effective vulnerability detection platform.. ICSE (Companion Volume), page 129-131. IEEE / ACM, (2019)A Quantitative Analysis Framework for Recurrent Neural Network., , , , , and . ASE, page 1062-1065. IEEE, (2019)Learning to Prevent Profitless Neural Code Completion., , , , , and . CoRR, (2022)Towards secure and robust stateful deep learning systems with model-based analysis. Nanyang Technological University, Singapore, (2020)CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning., , , , and . WWW, page 652-660. ACM, (2022)FuzzJIT: Oracle-Enhanced Fuzzing for JavaScript Engine JIT Compiler., , , , and . USENIX Security Symposium, page 1865-1882. USENIX Association, (2023)On the Importance of Building High-quality Training Datasets for Neural Code Search., , , , and . ICSE, page 1609-1620. ACM, (2022)Towards Building a Generic Vulnerability Detection Platform by Combining Scalable Attacking Surface Analysis and Directed Fuzzing.. ICFEM, volume 11232 of Lecture Notes in Computer Science, page 464-468. Springer, (2018)