Bibliographic record
Abstract
This research introduces the Holistic Cognitive Complexity (HCC) metric, an innovative object-oriented measurement method evolved from the traditional CB metric to offer a more comprehensive assessment of software complexity. By integrating additional complexity factors such as array usage and thread management, the HCC metric addresses both structural and cognitive complexities, providing a holistic view of modern software development. The practical utility of the HCC metric is demonstrated through a comparative analysis with the traditional CB metric, using a sample program to highlight the added intricacies captured by the HCC metric. The detailed complexity assessment facilitated by the HCC metric enables developers to identify potential problem areas early in the development process, guiding targeted refactoring and optimization efforts. This proactive identification of complexity hotspots is crucial for maintaining high standards of software quality and ensuring maintainability and scalability over time. Additionally, the HCC metric aids in better resource allocation and project planning by providing a more accurate measure of complexity, thus supporting effective project estimation and resource distribution. The significant contributions of the HCC metric include its ability to quantify the complexities introduced by advanced programming constructs like arrays and threads, offering deeper insights into software complexity than traditional metrics. This research underscores the importance of continuous enhancement of complexity metrics to address emerging challenges in software development. The HCC metric's robust framework and detailed assessment promise to elevate software quality, reliability, and maintainability, making it a valuable tool for software developers, analysts, and project managers.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".