The day’s strongest research treats large language models (LLMs) as bounded reviewers for software artifacts. QASecClaw, VulKey, and ACDL show the current emphasis: give the model a concrete finding, patch pattern, or…
LLM adoption looks most credible where the input is already bounded: one scanner alert, one workflow file checked against a fixed list, or one before-and-after refactoring pair.
The day’s strongest work makes AI coding more usable by narrowing where the model is allowed to improvise and by adding checks that run on real behavior.
The clearest near-term builds add hard structure around what the model is allowed to produce and keep verification active after generation.