On the predictability of Java byte codes (abstract) (poster session)
Bibliographic record
Abstract
Java byte codes are platform-independent. That means that any characterization of Java applications at the byte code execution level will reveal characteristics that any Java Virtual Machine will have to deal with, no matter whether this JVM is a Just-In-Time native code optimizing compiler running on a state-of-the-art high-performance workstation, or a byte code interpreter running in a watch.We believe that predictability profiles are particularly well-suited to capture and visualize program behavior, at a variable level of detail, as required by a systems architect interested in control flow, data flow, or automatic memory managment.We present predictability profiles for 6 SPECJVM98 programs, for three byte code sub traces.Subtrace:Invoke (polymorphic call target prediction)Load (load effective address prediction)New (new effective type prediction)For example, for Invoke byte codes, we measured the prediction rate achieved by invoke target predictors within every 20000 bytecodes of the first 2 million bytecodes executed using an unlimited, fully accurate BTB, and of Two-level predictors of path lengths 1,2,4,8, and 16. Prediction profiles for all these predictors are generally close together, but usually a BTB performs best in variable program phases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".