This trace follows the actual state transitions behind the companion When a Runtime Compiles Hot Code. It describes a common execution path; implementation details can vary, so keep the contract separate from the mechanism.
Step 1: Collect execution feedback
A just-in-time compiler translates code while a program runs. A runtime may begin by interpreting or compiling quickly, then use execution counters or profiling to identify frequently executed paths worth optimizing. The result can be specialized using assumptions about observed types and control flow.
Step 2: Identify a hot code path
If a function repeatedly receives integers, a JIT may generate a fast integer path guarded by a type check. If later calls use another type, the guard can fail and execution falls back or deoptimizes to a less specialized representation. This lets the runtime trade compilation cost for faster repeated work.
Step 3: Compile under assumptions
A JIT may specialize a hot function for observed types and emit guards; when an assumption fails, it deoptimizes or falls back while preserving program semantics.
At this point, record the state that changed and check the invariant before advancing. If the operation repeats, make clear which values persist and which are recomputed.
Step 4: Guard and deopt if needed
Warm-up behavior means short benchmarks can measure a different execution mode from long-lived production. Aggressive optimization can increase code size and compilation latency. A deoptimization is not necessarily a bug; it is a correctness-preserving response when an optimization assumption no longer holds.
Step 5: Measure warm execution
Design a benchmark to compare an interpreter and a JIT fairly. Explain why including startup and warm-up in one run can answer a different question than steady-state throughput.
The trace is complete when the result satisfies the stated contract. Compare this model with the concrete runtime or system you are studying before making a performance claim.