A quantitative analysis of Java bytecode sequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A variety of studies have been performed which analyse the bytecodes executed by a Java Virtual Machine (JVM). The simplest of these studies perform a static analysis of the bytecodes in the classes that make up the program [1]. Other studies have examined the dynamic behaviour of the program, only considering those individual bytecodes that are actually executed [2, 3]. Dynamic studies have also been extended to determine which bytecode pairs are commonly executed [4]. This work builds on these previous studies by extending the dynamic analysis from bigrams (bytecode pairs) to multicodes (variable length sequences) up to 20 bytecodes in length. The algorithms used to determine the multicodes are presented in addition to the most commonly occurring multicodes for a selection of benchmarks. Determining these multicodes is relevant to research into instruction set design. It is also directly applicable to interpreter optimization techniques such as super operators [5] and just-in-time compiler optimization techniques including bytecode idioms [6].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it