Innovating Software Complexity Measurement: A New Dimension in OO-Based Metrics
Bibliographic record
Abstract
This paper introduces a novel software complexity metric tailored for object-oriented programming called the Weighted Complexity (WC) metric. Addressing the growing intricacies of modern software systems, the WC metric integrates multiple dimensions of complexity, including structural, data, control, systemic, and cognitive aspects. By considering factors such as the nesting level of control structures, type of control structures, try-catch blocks, threads, and dynamic memory access, this metric offers a comprehensive evaluation of code complexity. The WC metric is demonstrated through a detailed computation example illustrating its practical application and benefits. The proposed metric not only enhances the understanding of software complexity but also provides actionable insights for improving code maintainability and reliability. Integration with continuous integration/continuous deployment pipelines, static code analysis tools, and agile development practices further underscores its utility in real-world software development environments. By offering a multifaceted approach to complexity assessment, the WC metric aims to enrich the toolkit of software engineers, promoting robust development practices and higher quality software systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".