Performance microbenchmarking is essential for ensuring software quality by providing granular insights into code efficiency. While automated performance microbenchmark generation tools (e.g., ju2jmh) are proposed to alleviate practitioners from manually curating microbenchmarks, the high volume of generated benchmarks can lead to protracted benchmarking execution time, as many of the generated benchmarks are too short in nature to be valuable for evaluating performance. In this paper, we present a novel approach that optimizes microbenchmark execution through a batching strategy, i.e., grouping benchmarks with similar code coverage and treating them as a single unit to 1) reduce execution overhead and 2) reduce the bias from microbenchmarks that are too short. We evaluate the effectiveness of this enhancement across various Java projects, comparing the execution times of clustered and individual micro benchmarks. Our findings demonstrate substantial improvements in execution efficiency, reducing execution time by up to 89.81% while preserving high microbenchmark stability.

Performance is a crucial non-functional requirement of many software systems. Despite the widespread use of performance testing, developers still struggle to construct and evaluate the quality of performance tests. To address these two major challenges, we implement a framework, dubbedju2jmh, to automatically generate performance microbenchmarks from JUnit tests and use mutation testing to study the quality of generated microbenchmarks. Specifically, we compare ourju2jmhgenerated benchmarks to manually written JMH benchmarks and to automatically generated JMH benchmarks using the AutoJMH framework, as well as directly measuring system performance with JUnit tests. For this purpose, we have conducted a study on three subjects (Rxjava,Eclipse-collections, andZipkin) with$\sim$454Ksource lines of code(SLOC), 2,417 JMH benchmarks (including manually written and generated AutoJMH benchmarks) and 35,084 JUnit tests. Our results show that theju2jmhgenerated JMH benchmarks consistently outperform using the execution time and throughput of JUnit tests as a proxy of performance and JMH benchmarks automatically generated using the AutoJMH framework while being comparable to JMH benchmarks manually written by developers in terms of tests’ stability and ability to detect performance bugs. Nevertheless,ju2jmhbenchmarks are able to cover more of the software applications than manually written JMH benchmarks during the microbenchmark execution. Furthermore,ju2jmhbenchmarks are generated automatically, while manually written JMH benchmarks require many hours of hard work and attention; therefore our study can reduce developers’ effort to construct microbenchmarks. In addition, we identify three factors (too low test workload, unstable tests and limited mutant coverage) that affect a benchmark's ability to detect performance bugs. To the best of our knowledge, this is the first study aimed at assisting developers in fully automated microbenchmark creation and assessing microbenchmark quality for performance testing.