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Characterizing Type Feedback in Just-in-Time Compilation (Artifact)

Authors Sebastián Krynski , Filip Říha , Filip Křikava , Jan Vitek

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DARTS.12.1.4.pdf
  • Filesize: 0.52 MB
  • 2 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Software and its engineering → Just-in-time compilers
  • Software and its engineering → Dynamic analysis
Keywords
  • Feedback vector
  • JIT compilation
  • type speculation
  • deoptimization

Metrics

Artifact

Artifact Evaluation Policy

The artifact has been evaluated as described in the ECOOP 2026 Call for Artifacts and the ACM Artifact Review and Badging Policy.

Abstract

This artifact provides the means to validate and reproduce the results of the associated paper "Characterizing Type Feedback in Just-in-Time Compilation". It enables to reproduce experimental claims made in the paper, including the claimed numbers and contained figures.

Cite As Get BibTex

Sebastián Krynski, Filip Říha, Filip Křikava, and Jan Vitek. Characterizing Type Feedback in Just-in-Time Compilation (Artifact). In Special Issue of the 40th European Conference on Object-Oriented Programming (ECOOP 2026). Dagstuhl Artifacts Series (DARTS), Volume 12, Issue 1, pp. 4:1-4:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/DARTS.12.1.4

Author Details

Sebastián Krynski
  • Czech Technical University, Prague, Czechia
Filip Říha
  • Czech Technical University, Prague, Czechia
Filip Křikava
  • Czech Technical University, Prague, Czechia
Jan Vitek
  • Charles University, Prague, Czech Republic
  • Czech Technical University, Prague, Czechia

Acknowledgements

This work was supported by the Czech Science Foundation grant No. 23-07580X.

Related Article

  • Sebastián Krynski, Filip Říha, Filip Křikava, and Jan Vitek, "Characterizing Type Feedback in Just-In-Time Compilation", in 40th European Conference on Object-Oriented Programming (ECOOP 2026), LIPIcs, Vol. 372, pp. 16:1-16:22, 2026. https://doi.org/10.4230/LIPIcs.ECOOP.2026.16

References

  1. M. Hahsler. Recommenderlab: An R framework for developing and testing recommendation algorithms. CoRR, 2022. URL: https://doi.org/10.48550/arXiv.2205.12371.
  2. Dirk Merkel. Docker: lightweight Linux containers for consistent development and deployment. Linux J., 2014(239), 2014. Google Scholar
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