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Get Started Free →Automates benchmark test creation for Go projects using the standard testing package with consistent software testing patterns. Use when creating performance benchmarks, profiling tests, or when the user mentions benchmarking, performance testing, or optimization.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 80% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 116% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 115% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 101% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 95% | 0% |
Instructions for AI coding agents on automating benchmark test creation using consistent software testing patterns in this Go project.
> Benchmark tests measure the execution time and memory allocation of functions, providing quantifiable metrics for performance analysis.
> Continuous benchmarking helps identify performance regressions early in the development cycle before they reach production.
> Benchmark results guide optimization efforts by identifying bottlenecks and quantifying the impact of performance improvements.
> Benchmarks enable comparison of different implementations or algorithms to make informed decisions about performance trade-offs.
> Memory allocation tracking helps identify unnecessary allocations and optimize memory usage patterns.
The FIRST principles for benchmark testing focus on creating reliable and meaningful measurements.
> Benchmark setup and teardown should be minimal and excluded from timing to ensure accurate measurement of the function under test.
> Each benchmark should be self-contained and not depend on shared state or results from other benchmarks to ensure isolated performance measurements.
> Benchmarks should produce consistent, comparable results across runs and environments by controlling inputs and avoiding non-deterministic operations.
> Benchmarks should optionally validate results to prevent the compiler from optimizing away the code under measurement.
> Benchmarks should be established before optimization work begins to provide a performance baseline and measure the impact of changes.
Microbenchmarking is a software testing technique that measures the performance of small, isolated code units to identify performance characteristics and bottlenecks.
Comparative Benchmarking is a testing approach that compares the performance of different implementations or algorithms side-by-side using consistent workloads.
Memory Profiling is the process of measuring memory allocations and usage patterns during benchmark execution using -benchmem flag.
Statistical Benchmarking uses multiple iterations to calculate statistical measures (mean, variance) to ensure reliable and reproducible results.
Sub-benchmarks organize related benchmark cases using b.Run() to group variations of the same function with different input scenarios.
Table-Driven Testing is a software testing technique in which benchmark cases are organized in a tabular format to systematically cover different input scenarios.
Identify performance-critical functions in pkg/ or internal/ that benefit from performance tracking (e.g., pkg/<package>/<file>.go).
Create benchmark tests in the same package (e.g., pkg/<package>/<file>_test.go).
Focus on functions that:
Structure all benchmark tests using the template pattern.
Establish performance baselines by running benchmarks on stable code before making changes.
| Command | Description | | --------------------------------------------------------------- | -------------------------------------------------- | | make go-test-bench | Execute all benchmarks with memory statistics | | go test -bench=BenchmarkPercent -benchmem ./pkg/percent | Execute a specific benchmark function | | go test -bench=. -benchmem -cpuprofile=cpu.prof ./pkg/percent | Generate CPU profile for performance analysis | | go test -bench=. -benchmem -memprofile=mem.prof ./pkg/percent | Generate memory profile for allocation analysis | | go test -bench=. -benchtime=10s ./pkg/percent | Run benchmarks for a specific duration | | benchstat old.txt new.txt | Compare benchmark results before and after changes |
> Use the standard Go testing package with testing.B for benchmark tests.
> Include testing and any packages needed for the function under test.
> Name benchmark functions with the Benchmark prefix followed by the function name (e.g., BenchmarkPercent for testing Percent()).
> Use b.Loop() to control the number of iterations. The testing framework automatically adjusts the loop iterations to get reliable timing measurements. b.Loop() is preferred over b.N as it provides better integration with the testing framework and more accurate measurements. Unlike b.N-style benchmarks, b.Loop() integrates timer management, it automatically handles b.ResetTimer() at the loop's start and b.StopTimer() at its end, eliminating the need to manually manage the benchmark timer for setup and cleanup code.
> When using b.Loop(), timer management is automatic and no manual b.ResetTimer(), b.StopTimer(), or b.StartTimer() calls are needed for typical benchmarks. For advanced scenarios not using b.Loop(), use b.ResetTimer() to exclude setup time from measurements and b.StopTimer()/b.StartTimer() to exclude specific operations.
> Use b.Run() to organize related benchmark cases with different input scenarios. Each sub-benchmark runs independently with its own b.N iterations.
> Use b.ReportAllocs() to report memory allocations per operation when not using -benchmem flag.
> Optionally validate results in benchmarks to prevent compiler optimizations from eliminating dead code.
Use this template for new benchmark test functions. Replace placeholders with actual values and adjust as needed for the use case.
For benchmarking multiple scenarios or input variations, use sub-benchmarks with table-driven approach.
gofunc Benchmark<FunctionName>(b *testing.B) { // Define benchmark cases with different scenarios benchmarks := []struct { name string param1 <type> param2 <type> // Add more parameters as needed }{ { name: "scenario description 1", param1: <value1>, param2: <value2>, }, { name: "scenario description 2", param1: <value1>, param2: <value2>, }, // Add more benchmark cases } for _, bm := range benchmarks { b.Run(bm.name, func(b *testing.B) { // Arrange // Setup code here (automatically excluded from timing by b.Loop) // Act for b.Loop() { _, _ = <Function>(bm.param1, bm.param2) } }) } }
For benchmarking a single scenario, use a simple loop without sub-benchmarks.
gofunc Benchmark<FunctionName>(b *testing.B) { // Arrange // Setup code here (automatically excluded from timing by b.Loop) param1 := <value1> param2 := <value2> // Act for b.Loop() { _, _ = <Function>(param1, param2) } }
For benchmarks that need to prevent compiler optimizations, store results in package-level variables.
govar ( benchResult <type> benchError error ) func Benchmark<FunctionName>(b *testing.B) { // Arrange // Setup code here (automatically excluded from timing by b.Loop) param1 := <value1> param2 := <value2> // Act for b.Loop() { benchResult, benchError = <Function>(param1, param2) } }
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