Xcode Compilation Analyzer

Xcode Compilation Analyzer

Use this skill when compile time, not just general project configuration, looks like the bottleneck.

Core Rules

• Start from evidence, ideally a recent .build-benchmark/ artifact or raw timing-summary output.
• Prefer analysis-only compiler flags over persistent project edits during investigation.
• Rank findings by expected wall-clock impact, not cumulative compile-time impact. When compile tasks are heavily parallelized (sum of compile categories >> wall-clock median), note that fixing individual hotspots may improve parallel efficiency without reducing build wait time.
• When the evidence points to parallelized work rather than serial bottlenecks, label recommendations as "Reduces compiler workload (parallel)" rather than "Reduces build time."
• Do not edit source or build settings without explicit developer approval.

What To Inspect

• Build Timing Summary output from clean and incremental builds
• long-running CompileSwiftSources or per-file compilation tasks
• SwiftEmitModule time -- can reach 60s+ after a single-line change in large modules; if it dominates incremental builds, the module is likely too large or macro-heavy
• Planning Swift module time -- if this category is disproportionately large in incremental builds (up to 30s per module), it signals unexpected input invalidation or macro-related rebuild cascading
• ad hoc runs with:
  • -Xfrontend -warn-long-expression-type-checking=<ms>
  • -Xfrontend -warn-long-function-bodies=<ms>
• deeper diagnostic flags for thorough investigation:
  • -Xfrontend -debug-time-compilation -- per-file compile times to rank the slowest files
  • -Xfrontend -debug-time-function-bodies -- per-function compile times (unfiltered, complements the threshold-based warning flags)
  • -Xswiftc -driver-time-compilation -- driver-level timing to isolate driver overhead
  • -Xfrontend -stats-output-dir <path> -- detailed compiler statistics (JSON) per compilation unit for root-cause analysis
• mixed Swift and Objective-C surfaces that increase bridging work

Analysis Workflow

1. Identify whether the main issue is broad compilation volume or a few extreme hotspots.
2. Parse timing-summary categories and rank the biggest compile contributors.
3. Run the diagnostics script to surface type-checking hotspots:

   python3 scripts/diagnose_compilation.py \
     --project App.xcodeproj \
     --scheme MyApp \
     --configuration Debug \
     --destination "platform=iOS Simulator,name=iPhone 16" \
     --threshold 100 \