Scaling Inference Efficiency of Small Language Models for Code Weakness Detection

This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does the inference efficiency (throughput, latency) of SLMs trained for CWE detection scale with model size when benchmarked on a private codebase, and how does this compare to larger models. Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. 17 claims were extracted from source literature; 16 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How does the inference efficiency (throughput, latency) of SLMs trained for CWE detection scale with model size when benchmarked on a private codebase, and how does this compare to larger models?

Autonomous literature synthesis. Automated review score: 7.8/10. Full text and citation available at Assignee Research.