Do crosscutting concerns cause modularity problems?
Bibliographic record
Abstract
It has been claimed that crosscutting concerns are pervasive and problematic, leading to difficulties in program comprehension, evolution, and long-term design degradation. To consider whether this theory bears out, we examine the patch history of the Mozilla project over a period of a decade to consider whether crosscutting concerns exist therein and whether we can see evidence of problems arising from them. Mozilla is an interesting case, due to its longevity; size; polylingual nature; and use of a patch review process, which maintains strong connections between issue reports and the patches that are intended to address each. We perform several statistical analyses of the over 200,000 patches submitted to address over 90,000 issues reported in this time period. We find that 90% of patches show little or no evidence of scattering, that the scattering of a patch tends to decrease slightly upon review on average, and that the system shows at worst a slow increase of average scattering over time.
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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 | 0.014 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".