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          <h1 class="title is-1">Resources</h1>
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            Interested in AI code auditing? Check out our resources below.
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      <p>
        We have published a series of LLM-based static analysis works that form the cornerstone of RepoAudit. Meanwhile, we continuously collect and categorize the latest research. The research projects and paper list below are provided for reference to researchers and practitioners in the field.
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        <br>
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      <!-- Resource Card 6: CodeLLMPaper -->
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              <a href="https://github.com/PurCL/CodeLLMPaper" target="_blank">
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              <p>
                <strong><a href="https://github.com/PurCL/CodeLLMPaper" target="_blank">
                  CodeLLMPaper: A Continuously Updated Collection of CodeLLM Papers</a></strong>
                <a href="https://github.com/PurCL/CodeLLMPaper" target="_blank">
                  <img src="https://img.shields.io/github/stars/PurCL/CodeLLMPaper" width="100" height="20" alt="GitHub Stars"/>
                </a><br>
                <p>CodeLLMPaper is a curated collection of the latest research on LLM-for-Code published in top-tier venues in software engineering, programming languages, security, NLP, and machine learning. The collected research works cover diverse coding
                tasks, foundational principles of code models, empirical studies, and surveys.</p>
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      <!-- Resource Card 5: BugScope -->
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            <p class="image is-128x128">
              <a href="https://arxiv.org/abs/2507.15671" target="_blank">
                <img src="./static/images/BugScope.png" alt="BugScope Logo">
              </a>
            </p>
          </figure>
          <div class="media-content">
            <div class="content">
              <p>
                <strong><a href="https://arxiv.org/abs/2507.15671" target="_blank">
                  BugScope: Learn to Find Bugs Like Human</a></strong>
                <p>BugScope is an intelligent bug detection agent that learns to identify diverse bugs from examples. It outperforms existing industrial tools like Cursor BugBot and CodeRabbit, detecting twice as many bugs while maintaining high precision.</p>
              </p>
            </div>
          </div>
        </article>
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      <!-- Resource Card 4: RFCScan -->
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          <figure class="media-left">
            <p class="image is-128x128">
              <a href="https://arxiv.org/pdf/2506.00714" target="_blank">
                <img src="./static/images/RFCScan.png" alt="RFCScan Logo">
              </a>
            </p>
          </figure>
          <div class="media-content">
            <div class="content">
              <p>
                <strong><a href="https://arxiv.org/pdf/2506.00714" target="_blank">
                  An LLM Agent for Functional Bug Detection in Network Protocols</a></strong>
                <a href="https://github.com/zmw12306/RFCScan" target="_blank">


                  <img src="https://img.shields.io/github/stars/zmw12306/RFCScan" width="100" height="20" alt="GitHub Stars"/>
                </a><br>
                <p>An LLM agent for statically detecting functional bugs in network protocol implementations against the natural language specifications in the RFC documents</p>
              </p>
            </div>
          </div>
        </article>
      </div>

      <!-- Resource Card 3: ParCleanse -->
      <div class="column is-12">
        <article class="media">
          <figure class="media-left">
            <p class="image is-128x128">
              <a href="https://arxiv.org/pdf/2504.18050" target="_blank">
                <img src="./static/images/ParCleanse.png" alt="ParCleanse Logo">
              </a>
            </p>
          </figure>
          <div class="media-content">
            <div class="content">
              <p>
                <strong>
                  <a href="https://arxiv.org/pdf/2504.18050" target="_blank">
                    Validating Network Protocol Parsers with Traceable RFC Document Interpretation
                  </a>
                </strong>
                <a href="https://github.com/zmw12306/ParCleanse/tree/main" target="_blank">
                  <img src="https://img.shields.io/github/stars/zmw12306/ParCleanse" width="100" height="20" alt="GitHub Stars"/>
                </a><br>
                ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2025)<br>
                <p>
                  An LLM-driven test case generation technique that uncovers the inconsistencies between network protocol parsers and the packet format specifications in RFC documents.
                </p>
              </p>
            </div>
          </div>
        </article>
      </div>

      <!-- Resource Card 2: LLMDFA -->
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            <p class="image is-128x128">
              <a href="https://neurips.cc/virtual/2024/poster/95227" target="_blank">
                <img src="./static/images/LLMDFA.jpg" alt="LLMDFA Logo">
              </a>
            </p>
          </figure>
          <div class="media-content">
            <div class="content">
              <p>
                <strong><a href="https://neurips.cc/virtual/2024/poster/95227" target="_blank">
                  LLMDFA: Analyzing Dataflow in Code with Large Language Models</a></strong>
                <a href="https://github.com/chengpeng-wang/LLMDFA" target="_blank">
                  <img src="https://img.shields.io/github/stars/chengpeng-wang/LLMDFA" width="100" height="20" alt="GitHub Stars"/>
                </a><br>
                The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024)<br>
                <p>An LLM-powered summary-based data-flow analysis framework, achieving comparable and even superior precision and recall to state-of-the-art symbolic static analysis tools.</p>
              </p>
            </div>
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        </article>
      </div>

      <!-- Resource Card 1: LLMSAN -->
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            <p class="image is-128x128">
              <a href="https://aclanthology.org/2024.findings-emnlp.217/" target="_blank">
                <img src="./static/images/LLMSAN.jpg" alt="LLMSAN Logo">
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              <p>
                <strong><a href="https://aclanthology.org/2024.findings-emnlp.217/" target="_blank">
                  LLMSAN: Sanitizing Large Language Models in Bug Detection with Data-Flow</a></strong>
                <a href="https://github.com/chengpeng-wang/LLMSAN" target="_blank">
                  <img src="https://img.shields.io/github/stars/chengpeng-wang/LLMSAN" width="100" height="20" alt="GitHub Stars"/>
                </a><br>
                The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings 2024)<br>
                <p>A sanitization technique that mitigates LLM hallucinations in bug detection, validating data-flow paths as verifiable chain-of-thoughts via divide-and-conquer.</p>
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