Measurement
Bibliographic record
Abstract
Abstract This article discusses the following topics: History of software measurement Definition and goals of software measurement Types of software models and measures Examples of descriptive and predictive models and measures Measurement methodology Measurement was an important activity from the earliest examples of computer programming. Early programs were often developed to perform repetitive calculations such as computing firing tables for military applications; to implement numerical methods for solving mathematical problems; to process business transactions and update files; and to develop systems software such as device drivers, assemblers, compilers, and operating systems. The measures of interest were specific to the program, and were strongly influenced by the resource limitations of the times. Programmers were concerned mostly with implementing the program correctly, improving the execution speed of their programs, and conserving limited fast memory on the machines. By the time of the influential conference at Garmisch, Germany, which introduced the term “software engineering” in 1968, the scope of software measurement had increased. For example, discussions at the conference reflect that the properties of productivity and reliability were recognized, along with the context of developing software by a group of people rather than an individual. Continuing from the late 1960s, through the 1970s and early 1980s, software measurement was marked by selected instances of progress. However, the measurement goals were neither explicit nor comprehensive. Progress since 1985 or so has brought software measurement to a more mature state with the following characteristics of this nature state are defined.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.152 | 0.090 |
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".