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Using application benchmark call graphs to quantify and improve the practical relevance of microbenchmark suites.

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This study enhances software performance testing by optimizing microbenchmarks. It identifies and removes redundant tests while recommending new ones to cover critical application functions, reducing undetected performance issues.

Keywords:
BenchmarkingMicrobenchmarkingPerformance testingTime series databases

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Performance issues in applications necessitate early detection upon code modification.
  • Application benchmarks and microbenchmarks are used in build pipelines to ensure performance goals, but have limitations.

Purpose of the Study:

  • To improve the practical relevance and verification of microbenchmark suites using application flow data.
  • To reduce the risk of undetected performance problems by optimizing microbenchmark suites.

Main Methods:

  • Determining overlap of common function calls between application and microbenchmarks.
  • Identifying redundant microbenchmarks.
  • Recommending relevant functions not yet covered by microbenchmarks.

Main Results:

  • Microbenchmark suites covered 35.62% to 66.29% of functions called during application benchmarks.
  • Redundancy removal reduced microbenchmark suites to ~10% and ~23% of their original size.
  • Recommendations identified up to 26 and 14 new functions for benchmarking.

Conclusions:

  • Optimized microbenchmark suites can effectively test relevant functions after code changes.
  • The approach enhances software performance assurance by integrating microbenchmarks and application benchmarks.
  • Utilizing synergies between different performance test granularities improves overall software quality.