What's Wrong with My Benchmark Results? Studying Bad Practices in JMH Benchmarks
Bibliographic record
Abstract
Microbenchmarking frameworks, such as Java's Microbenchmark Harness (JMH), allow developers to write fine-grained performance test suites at the method or statement level. However, due to the complexities of the Java Virtual Machine, developers often struggle with writing expressive JMH benchmarks which accurately represent the performance of such methods or statements. In this paper, we empirically study bad practices of JMH benchmarks. We present a tool that leverages static analysis to identify 5 bad JMH practices. Our empirical study of 123 open source Java-based systems shows that each of these 5 bad practices are prevalent in open source software. Further, we conduct several experiments to quantify the impact of each bad practice in multiple case studies, and find that bad practices often significantly impact the benchmark results. To validate our experimental results, we constructed seven patches that fix the identified bad practices for six of the studied open source projects, of which six were merged into the main branch of the project. In this paper, we show that developers struggle with accurate Java microbenchmarking, and provide several recommendations to developers of microbenchmarking frameworks on how to improve future versions of their framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".