Metrology, measurement and metrics in software engineering
Bibliographic record
Abstract
Up until recently software 'metrics' have been most often proposed as the quantitative tools of choice in software engineering, and the analysis of these had been most often discussed from the perspective referred to as 'measurement theory'. However, in other disciplines, it is the domain of knowledge referred to as 'metrology' that is the foundation for the development and use of measurement instruments and measurement processes. The IEEE-computer society, with the support of a consortium of industrial sponsors, has recently published a guide to the Software Engineering Body of Knowledge (SWEBOK) and, throughout this guide, measurement is pervasive as a fundamental engineering tool. We use our initial modelling of the sets of measurement concepts documented in the ISO international vocabulary of basic and general terms in metrology to investigate and position the measurement concepts within this body of knowledge, and to identify gaps where further research on software measurement is required.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".