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  "paper_id": "uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis",
  "page_url": "https://naser.github.io/research-publications/papers/uncovering-steady-state-executions-in-java-microbenchmarking-with-call-graph-analysis/",
  "title": "Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis",
  "title_variants": [],
  "authors": [
    "Madeline Janecek",
    "Sneh Patel",
    "Naser Ezzati-Jivan"
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  "publication": {
    "year": 2023,
    "venue": "ACM/SPEC ICPE Companion",
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    "publication_date": "2023-04-15",
    "online_date": "2023-04-15",
    "print_date": "2023-04-15",
    "volume": null,
    "issue": null,
    "pages": "71-75",
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    "publisher": "ACM",
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  "identifiers": {
    "doi": "10.1145/3578245.3584689"
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  "abstract": null,
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  "description": "The paper uses sampled call graphs and machine learning to distinguish warmup from steady-state executions in Java microbenchmarks.",
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    "core_contribution": "The paper uses sampled call graphs and machine learning to distinguish warmup from steady-state executions in Java microbenchmarks.",
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    "method": "Linux perf samples call stacks throughout JMH executions; timestamped start/end logs (Apache Log4j 2.11.2) delimit executions. Call graphs are visualized as flame graphs and embedded with Graph2Vec. PELT change-point detection supplies labels; this study checks 50 similar executions (5% of 1,000) after the change point rather than 500. MLP, Random Forest, Decision Tree, KNN, and SVC classifiers are evaluated (pp. 2-4).",
    "findings": "The workload is a simple Java binary search with 10,000 executions across 10 forks on Ubuntu 22.04 LTS, 64-bit kernel 5.15.0-56, Perf 5.15.64, and JMH; warmup graphs have deeper/more irregular stacks and more interpreter functions. Accuracy/precision/recall: MLP 88.5/31.6/12.5%, RF 90.4/75.0/6%, DT 82.8/19.3/22.5%, KNN 89.8/45/9%, SVC 90/50/1% (pp. 3-4).",
    "limitations": "Accuracy/precision are misleading without the very low recall; the paper attributes this partly to the small dataset/labels and possibly insufficient Graph2Vec vector size. Cross-JVM and cross-workload generalization are not established.",
    "future_work": "More benchmarks, forks, and iterations; alternate/larger graph vectorizations; and unsupervised learning to avoid hand-derived labels (p. 4)."
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  "tags": [
    "performance-analysis",
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    "machine-learning",
    "trace-analysis"
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    "Java",
    "JMH",
    "microbenchmarking",
    "steady-state detection",
    "warmup",
    "call-graph analysis",
    "Perf",
    "Graph2Vec",
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      "title": "Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis",
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      "title": "Uncovering Steady State Executions in Java Microbenchmarking with Call Graph Analysis",
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    "code": "https://github.com/sneh2001patel/Uncovering-Steady-State-Executions-in-Java-Microbenchmarking-with-Callgraph-Analysis",
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