Predictors of Software Metric Correlation: A Non-parametric Analysis
Bibliographic record
Abstract
A number of authors hypothesize and experimentally confirm that Cyclomatic Complexity (CC) has a very strong correlation with Lines of Code (LOC), justifying the use of LOC in place of CC. Others report on a moderate correlation and advocate for the use of both metrics. These studies have, for the most part, studied production code, and we suspect different results may be observed for test code. With 40 different, large open-source subjects and five subjects from industry partners, we collected metric values for LOC, CC and Halstead Effort (HE) and measured their correlation. In test code, contrary to production code, there exist a very weak (or almost no) correlation between (a) LOC and CC, and weak (nearly moderate) correlation for (b) HE and CC, and (c) LOC and HE. We therefore argue and propose that the level of correlation depends on at least three factors namely: the kind of code (i.e., production code vs test code), the kind of software (open-source vs industry) and the kind of metric (LOC, CC, HE). Given the weak monotonicity between CC, LOC and HE we observe, we aspire to challenge the viewpoint that CC and Halstead metrics are redundant with LOC, as some studies suggest, at least on test code. We therefore advocate for using CC over LOC (or both, or cyclomatic density) when studying test code, as CC is perceived to better reflect cognitive complexity, numerical complexity, interdependency and code refactoring that cannot be accounted for simply by LOC.
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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 | 0.029 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".