A Metrics Suite for Measuring Indirect Coupling Complexity
Bibliographic record
Abstract
Abstract Software development can be a time-consuming and costly process that requires a significant amount of effort. Developers are often tasked with completing programming tasks or making modifications to existing code without increasing overall complexity. It is essential for them to understand the dependencies between the program components before implementing any changes. However, as code evolves, it becomes increasingly challenging for project managers to detect indirect coupling links between components. These hidden links can complicate the system, cause inaccurate effort estimates, and compromise the quality of the code. To address these challenges, this study aims to provide a set of measures that leverage measurement theory and hidden links between software components to expand the scope, effectiveness, and utility of accepted software metrics. The research focuses on two primary topics: (1) how indirect coupling measurements can aid developers with maintenance tasks and (2) how indirect coupling metrics can quantify software complexity and size, leveraging weighted differences across techniques. The study presents a comprehensive set of measures designed to assist developers and project managers with project management and maintenance activities. Using the power of indirect coupling measurements, these measures can enhance the quality and efficiency of software development and maintenance processes.
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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 | 0.012 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".