Incremental Scanning With Baseline Comparison And Delta Reporting

AI-powered static analysis for security.

Unique: Implements baseline comparison at the Python CLI layer by storing and comparing JSON scan results, enabling incremental reporting without requiring the OCaml engine to maintain state. This design allows flexible baseline sources (local files, semgrep.dev API, git history) while keeping the core scanning engine stateless.

vs others: Simpler than tools requiring full codebase re-analysis (like some SAST tools) because it compares results rather than re-running analysis; more practical than git-diff-based filtering because it handles line number shifts and can detect moved findings.

via “baseline comparison and incremental scanning”

Static analysis — custom rules for bugs and security, 30+ languages, AI-powered triage.

Unique: Compares current findings against baseline to report only new issues, with deduplication and status tracking, enabling practical incremental scanning in CI/CD without reporting pre-existing issues

vs others: More practical than reporting all findings on every commit; more efficient than re-analyzing unchanged code; enables focus on newly introduced issues

via “snapshot versioning and baseline management with rollback capability”

Visual testing platform with AI-powered regression detection.

Unique: Maintains complete version history of visual baselines linked to commits/PRs, enabling rollback and historical comparison. Percy automatically manages baseline branching for feature branches, eliminating manual baseline synchronization.

vs others: More sophisticated than BackstopJS's file-based baseline management (which requires manual Git tracking) and provides better audit trails than Chromatic's implicit baseline versioning; enables compliance-grade visual change tracking.

via “incremental graph update system with delta computation”

Local knowledge graph for Claude Code. Builds a persistent map of your codebase so Claude reads only what matters — 6.8× fewer tokens on reviews and up to 49× on daily coding tasks.

Unique: Implements delta-based incremental updates (diagram 4) that compute the difference between current and previous codebase states, then apply only necessary graph changes. The system uses SHA-256 hashing to detect file changes and identifies which entities were added/modified/deleted, reducing update time from O(n) to O(delta).

vs others: Faster than full re-indexing because it only re-parses changed files and updates affected graph nodes, whereas naive approaches would re-parse the entire codebase on every change.

via “incremental scanning and change-based vulnerability detection”

** - Enable AI agents to secure code with [Semgrep](https://semgrep.dev/).

Unique: MCP enables agents to pass file change lists to Semgrep, which filters rule execution to changed files only; combines change detection with pattern matching to provide fast, targeted vulnerability detection without full-codebase re-scanning

vs others: Faster than full-codebase scanning for CI/CD gates; more accurate than simple diff-based filtering because it understands code structure and can detect vulnerabilities in changed code that affects unchanged code

via “incremental codebase analysis with change-based violation detection”

Unique: Implements change-based incremental analysis that re-analyzes only modified files and their dependents, reducing analysis time from minutes to seconds. Most competitors (SonarQube, ESLint) perform full scans on every invocation; Codiga's incremental approach is more efficient for large codebases.

vs others: Significantly faster than full-scan competitors for large codebases, but less accurate for cross-file dependency analysis due to the incremental nature of the approach.