Towards effective assessment of steady state performance in Java software: Are we there yet?

TL;DR

This paper investigates the reliability of Java microbenchmark warmup estimation, revealing that benchmarks often do not reach steady state and developer estimates are frequently inaccurate, impacting performance assessment quality.

Contribution

It demonstrates that Java microbenchmarks often fail to reach steady state and that developer warmup estimates are frequently inaccurate, highlighting the need for automated solutions.

Findings

Many benchmarks do not reach steady state.

Developer warmup estimates are often inaccurate.

Dynamic reconfiguration improves warmup estimation accuracy.

Abstract

Microbenchmarking is a widely used form of performance testing in Java software. A microbenchmark repeatedly executes a small chunk of code while collecting measurements related to its performance. Due to Java Virtual Machine optimizations, microbenchmarks are usually subject to severe performance fluctuations in the first phase of their execution (also known as warmup). For this reason, software developers typically discard measurements of this phase and focus their analysis when benchmarks reach a steady state of performance. Developers estimate the end of the warmup phase based on their expertise, and configure their benchmarks accordingly. Unfortunately, this approach is based on two strong assumptions: (i) benchmarks always reach a steady state of performance and (ii) developers accurately estimate warmup. In this paper, we show that Java microbenchmarks do not always reach a steady state, and often developers fail to accurately estimate the end of the warmup phase. We found that a considerable portion of studied benchmarks do not hit the steady state, and warmup estimates provided by software developers are often inaccurate (with a large error). This has significant implications both in terms of results quality and time-effort. Furthermore, we found that dynamic reconfiguration significantly improves warmup estimation accuracy, but still it induces suboptimal warmup estimates and relevant side-effects. We envision this paper as a starting point for supporting the introduction of more sophisticated automated techniques that can ensure results quality in a timely fashion.