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Automated Large-Scale Multi-Language Dynamic Program Analysis in the Wild (Artifact)., , , , , , , and . Dagstuhl Artifacts Ser., 5 (2): 11:1-11:3 (2019)Quantifying the Brown Side of Priority Schedulers: Lessons from Big Clusters., , , , and . SIGMETRICS Perform. Evaluation Rev., 42 (3): 76-81 (2014)Optimizing type-specific instrumentation on the JVM with reflective supertype information., and . J. Vis. Lang. Comput., (2018)Renaissance: benchmarking suite for parallel applications on the JVM., , , , , , , , , and 2 other author(s). PLDI, page 31-47. ACM, (2019)Adaptive Thread Type Selection on the Java Virtual Machine., , , and . CANDARW, page 371-373. IEEE, (2023)Accurate Fork-Join Profiling on the Java Virtual Machine., , , , and . Euro-Par, volume 13440 of Lecture Notes in Computer Science, page 35-50. Springer, (2022)Renaissance: a modern benchmark suite for parallel applications on the JVM., , , , , , , , , and 2 other author(s). SPLASH (Companion Volume), page 11-12. ACM, (2019)Automatically Assessing and Extending Code Coverage for NPM Packages., , , and . AST@ICSE, page 40-49. IEEE, (2021)Automated Large-scale Multi-language Dynamic Program Analysis in the Wild., , , , , , , and . Software Engineering, volume P-310 of LNI, page 111. Gesellschaft für Informatik e.V., (2021)Actor profiling in virtual execution environments., , and . GPCE, page 36-46. ACM, (2016)