Empirical investigation of a novel approach to check the integrity of software engineering measuring processes
Bibliographic record
Abstract
We present an empirical investigation of the applicability of Benford's Law (1958) and Digital Statistics (Nigrine, 1995) in the context of software engineering metrics analysis and process validation. We have conducted an investigation to determine under what circumstances various software metrics follow Benford's Law, and whether any special characteristics, or irregularities, in the data can be uncovered if the data are found not to follow the law. Lists were formed from three software metrics extracted from 100 public domain industrial Java projects. These metrics were Lines of Code (LOC), Fan-Out (FO) and McCabe Cyclomatic Complexity (MCC). The results indicate that the first digits of numbers in lists of LOC metrics extracted from the projects closely followed the probabilities predicted by Benford's Law. The FO and MCC metrics did not follow the standard Benford's Law as closely as the LOC metrics.
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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 | 0.056 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".