An AI linter applies model-based analysis to source code and can check conventions, patterns or contextual concerns beyond a traditional deterministic linter. Teams can define review logic tailored to their code and ownership boundaries.
Model-based findings are probabilistic and should be evaluated for precision, severity and developer address rate. High-confidence deterministic rules should remain in ordinary static tools when possible.
ELI5
An AI linter reviews code and flags possible problems using a model, including some issues that are hard to express as fixed rules. Its suggestions can depend on the surrounding code and a team's own conventions.
For example, an AI linter might notice that a database query could expose private data even though the code is valid syntax. Because model findings can be wrong, teams should measure their accuracy and keep clear-cut checks in ordinary deterministic linters when possible.
How is an AI linter different from a traditional linter?
A traditional linter applies fixed rules, while an AI linter can interpret broader context but produces less deterministic findings.
When should a rule remain deterministic?
A rule should remain deterministic when it can be expressed reliably and checked cheaply without model ambiguity.
