Are patches cutting it?: structuring distribution within a JVM using aspects
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
Distribution is hard to modularize. Consequently, its addition to a software system can jeopardize fundamental software engineering principles such as maintainability, understandability and evolva-bility. The distributed Java Virtual Machine (dJVM) is a cluster aware implementation of a JVM, designed specifically for evaluating distrib-uted runtime support algorithms [1]. A prototype implementation of the dJVM relies on a patch file applied to IBM’s Jikes Research Virtual Machine (RVM) [6], introducing distribution code into roughly 55 % of the original 1500 files. An initial experiment using AspectJ [7] to in-troduce this same distribution code as aspects demonstrates the benefits of a modularized ap-proach versus the original patched approach. Pre-liminary results show that aspects can improve the overall quality of the implementation from a software engineering perspective. Specifically, the aspects improved the internal structure of dis-tribution code and made its external interaction explicit. Additionally, consolidating and structur-ing previously scattered code reduced its size by a factor of three.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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