semantic-conflict-prediction

To detect semantic code conflicts that textual merges miss, analyze code structure and agent intent using AST-based parsing and symbol-level claims. This approach surfaces signature changes, transitive dependency breaks, and renames that standard git merges cannot identify.

Semantic conflict prediction identifies mismatches like signature changes and dependency breaks before merging. Tree-sitter enables AST-based symbol extraction, parsing functions, methods, and classes across polyglot repositories to model agent intents and estimate transitive impact.

You coordinate concurrent agents by modeling their intents as read, modify, add, delete, or rename claims at the function level. Analyzing these symbol claims against an incremental dependency graph predicts blocking conflicts and prevents build breakages before work begins.

Yes, you can build and update incremental dependency graphs lazily to estimate the blast radius of function signature changes. By mapping call and type edges, the analysis calculates the transitive impact and scores conflict severity with confidence metrics.

The best way to prevent transitive dependency conflicts is to classify direct, dependency, and transitive conflicts using AST-based analysis before merging. This scoring mechanism evaluates severity and confidence, producing suggested resolutions to avoid runtime breakages.

Yes, semantic conflict prediction works for polyglot repositories by using tree-sitter to identify functions, methods, classes, and exported symbols across multiple languages. This AST-based extraction standardizes symbol-level claims for consistent conflict analysis.