The Theory of Relative Dependency: Higher Coupling Concentration in Smaller Modules
Bibliographic record
Abstract
Recent studies have repeatedly found that smaller modules are proportionally more defect-prone. In this article, the authors formulate and test a hypothesis stating that smaller modules are proportionally more coupled, given that dependencies caused by coupling have been consistently associated with defect-proneness. Strong evidence supports this hypothesis. Furthermore, refactoring exacerbates this effect. On the basis of this study's highly consistent results, the authors state the empirically based theory of relative dependency. That is, in large-scale software systems, smaller modules will be proportionally more dependent compared to larger ones. These findings have two implications for practice. First, we now have an empirically supported mechanism explaining the observations that defect concentration is higher in smaller modules. Practitioners can use this mechanism as evidence while seeking resources and support to revise or amend their organizations' quality assurance and quality control practices. Second, particularly for the projects that refactor extensively, such as those using agile methods, focusing defect detection activities on smaller modules will increase their efficiency and effectiveness even more.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".