Identifying, Assigning, and Quantifying Crosscutting Concerns
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
Crosscutting concerns degrade software quality. Before we can modularize the crosscutting concerns in our programs to increase software quality, we must first be able to find them. Unfortunately, accurately locating the code related to a concern is difficult, and without proper metrics, determining how much the concern is crosscutting is impossible. We propose a systematic methodology for identifying which code is related to which concern, and a suite of metrics for quantifying the amount of crosscutting code. Our concern identification and assignment guidelines resolve some of the ambiguity issues encountered by other researchers. We applied this approach to systematically identify all the requirement concerns in a 13,531 line program. We found that 95% of the concerns were crosscutting - indicating a significant potential for improving modularity - and that our metrics were better able to determine which concerns would benefit the most from reengineering.
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 | 0.001 | 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 | 0.000 |
| 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