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When Labels Are Scarce: A Systematic Mapping of Label-Efficient Code Vulnerability Detection

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arXiv:2604.00079v1 Announce Type: new Abstract: Machine-learning-based code vulnerability detection (CVD) has progressed rapidly, from deep program representations to pretrained code models and LLM-centered pipelines. Yet dependable vulnerability labeling remains expensive, noisy, and uneven across projects, languages, and CWE types, motivating approaches that reduce reliance on human labeling. This survey maps these approaches, synthesizing five paradigm families and the mechanisms they use. It

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Computer Science > Cryptography and Security [Submitted on 31 Mar 2026] When Labels Are Scarce: A Systematic Mapping of Label-Efficient Code Vulnerability Detection Noor Khalal, Chakib Fettal, Lazhar Labiod, Mohamed Nadif Machine-learning-based code vulnerability detection (CVD) has progressed rapidly, from deep program representations to pretrained code models and LLM-centered pipelines. Yet dependable vulnerability labeling remains expensive, noisy, and uneven across projects, languages, and CWE types, motivating approaches that reduce reliance on human labeling. This survey maps these approaches, synthesizing five paradigm families and the mechanisms they use. It connects mechanisms to token, graph, hybrid, and knowledgebased representations, and consolidates evaluation and reporting axes that limit comparison (label-budget specification, compute/cost assumptions, leakage, and granularity mismatches). A Design Map and constraintfirst Decision Guide distill trade-offs and failure modes for practical method selection. Subjects: Cryptography and Security (cs.CR); Software Engineering (cs.SE) Cite as: arXiv:2604.00079 [cs.CR] (or arXiv:2604.00079v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2604.00079 Focus to learn more Submission history From: Noor Khalal [view email] [v1] Tue, 31 Mar 2026 16:16:29 UTC (925 KB) Access Paper: view license Current browse context: cs.CR < prev | next > new | recent | 2026-04 Change to browse by: cs cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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