DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents
arXiv:2511.20709v2 Announce Type: replace-cross Abstract: Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge. We present DualGauge, the first fully automated framework for jointly evaluating correctness and security of specification-only code generation, supported by DualGauge-Bench, a language-agnostic benchmark of 307 coding tasks each paired with functional and security tests derived from the same specification. Evaluating 10 representa
The rapid deployment and increasing sophistication of LLMs for code generation necessitate robust evaluation frameworks to ensure their practical reliability and security.
This development addresses a critical gap in safely integrating AI-generated code, directly influencing the adoption and trustworthiness of LLMs in software development.
The introduction of automated, joint security-functionality benchmarking will accelerate the development of more reliable and secure AI coding tools, setting a new standard for their assessment.
- · AI-powered coding tool developers
- · Cybersecurity sector
- · Software developers
- · Enterprise AI adopters
- · Insecure AI coding solutions
- · Manual code auditing processes
- · Companies neglecting AI security
Automated code generation becomes more trustworthy and widespread due to improved reliability and security validation.
The demand for 'secure by design' AI code generation tools increases, pushing developers to integrate security from the outset.
Reduced attack surface in a wide range of software due to fewer vulnerabilities introduced by AI-generated code, although new attack vectors related to AI systems themselves may emerge.
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Read at arXiv cs.AI