Software estimation: universal models or multiple models?
Bibliographic record
Abstract
In the field, there is a very large diversity of development processes in use, and various mixes of costs drivers, each with a different impact depending on the context. The classical approach to building estimation models in software engineering is to build a single estimation model and include within it as many cost factors (i.e. independent variables) as possible. In this paper, we do not postulate that there exists a single estimation model that is ideal in all circumstances, but rather we report on exploratory research conducted over the past few years looking at relevant concepts from the field of economics and from discussions with organizations attempting to understand the data that they have collected on their projects. The purpose of exploratory research is not to demonstrate a hypothesis, but to identify new potentially relevant concepts to develop hypotheses to be tested later on with empirical or experimental data.
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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 | 0.026 | 0.095 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".