Results for “false-positive-reduction”

Verifies suspected security bugs to eliminate false positives, producing a TRUE POSITIVE or FALSE POSITIVE verdict with documented evidence for each.

Extract false-positive and false-negative gaps from VLM binary-classification-question predictions by comparing model responses against ground truth, producing a structured JSONL file and summary report for downstream root-cause analysis.

Triage mutation testing results by combining survived mutants, unnecessary test statements, and code graph analysis to identify false positives, missing test coverage, and fuzzing targets.