When Labels Are Scarce: A Systematic Mapping of Label-Efficient Code Vulnerability Detection
This work addresses the critical challenges in code vulnerability detection—namely, the scarcity of high-quality labels, substantial noise, and imbalanced data distributions—all of which heavily rely on costly manual annotation. The study presents the first systematic mapping framework for label-efficient vulnerability detection, organizing existing approaches into five paradigm families: weak supervision, self-supervision, transfer learning, and others. It further links these paradigms to diverse code representations, including token-based, graph-based, hybrid, and knowledge-enhanced forms. By introducing a design taxonomy and a constraint-prioritized decision guide, the paper clarifies the applicability and failure modes of each method, exposes key obstacles such as inconsistent evaluation protocols, and establishes a unified perspective for evaluating and selecting techniques under label-scarce conditions.