Are patches cutting it?: structuring distribution within a JVM using aspects
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".