In our study, a novel SAST-LLM mashup slashed false positives by 91% compared to a widely used standalone SAST tool.

Treating annotation as a data understanding problem, rather than a labeling workflow challenge, can systematically drive down error rates and reduce the time and cost of producing high-quality data ...

We show that, compared with surgeon predictions and existing risk-prediction tools, our machine-learning model can enhance ...