Beyond Traditional Metrics: Advancing Software Complexity Analysis with the ACB Metric
Bibliographic record
Abstract
This paper introduces the Advanced CB (ACB) metric, a novel approach to measuring software complexity within object-oriented programming environments. Building on the foundational CB metric, the ACB metric incorporates additional factors such as compound expressions and try-catch blocks, providing a more comprehensive assessment of cognitive complexity. By accurately quantifying these elements, the ACB metric aims to enhance developers’ understanding of software intricacies, promoting better design and maintenance practices. The potential to integrate the ACB metric with educational tools and language translation technologies underscores its utility in real-world software development. Results demonstrate that the ACB metric offers a nuanced understanding of complexity, thereby facilitating improved software quality and maintainability.
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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 | 0.011 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".