Bibliographic record
Abstract
The potential benefits of advanced separation of concerns (ASOC) techniques are well known and many programmers find the idea of using them appealing. For new software engineering projects these modularization mechanisms offer guidelines of how to structure the system modules. But how can legacy systems profit from them? Code related to concerns not represented in the current modularization has to be carefully identified and extracted while preserving system integrity. This paper presents a refactoring tool that aids in the extraction of concerns that are ill-represented in the prevalent OOP decomposition 1. Mining for Concerns While contemporary modularization techniques such as OOP have proven to be successful, their approach of modularizing software systems according to a single concern is inherently insufficient and might not provide enough structure for developing complex systems [6, 7, 8]. Concerns not represented in the current system decomposition can decrease the code quality, as they have to be “pressed ” into the primary decomposition. We call such concerns hidden concerns (HCs). Code related to these concerns can show two symptoms of poor modularity: it can be scattered over the whole project or it can be tangled with other code. Code tangling is a state where lines related to different concerns are interwoven. ASOC techniques promise to overcome these problems by providing constructs to represent otherwise hidden concerns. However, regardless of which ASOC technique is used, software developers face the same problems when applying these paradigms to legacy systems: How to identify and extract the code related to a hidden concern? Due to the scattered nature of hidden concerns, searching for them in existing code is a non-trivial task.
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 | 0.005 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".