Bibliographic record
Abstract
Middleware technologies such as Web Services, CORBA and DCOM have been very successful in solving distributed computing problems for a large family of application domains. As middleware systems are getting widely adopted and more functionally mature, it is also increasingly difficult for the architecture of middleware to achieve a high level of adaptability and configurability, due to the limitations of traditional software decomposition methods. Aspect oriented programming has brought us new design perspectives because it permits the superimpositions of multiple abstraction models on top of one another. It is a very powerful technique in separating and simplifying design concerns. In this paper, we first show that, through the quantification of aspects in the legacy implementations, the modularity of middleware architecture is greatly hindered by the ubiquitous existence of tangled logic. We then go one step further by factoring out a number of aspects identified in the mining work and re-implementing them as aspect programs. The aspect oriented re-factorization allows us to apply a set of software engineering metrics to quantify the changes of the re-factored system in both the structural complexity and the runtime performance. The aspect oriented re-factoring proves that the aspect oriented programming is capable of composing orthogonal design requirements. The final "woven" system is able to correctly provide both the fundamental functionality and the "aspectized" functionality with negligible overhead and a leaner architecture. Further more, the configurability of middleware is dramatically increased because the "aspectized" features can be configured in and out during the compile-time
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".