Bibliographic record
Abstract
Abstract This article presents a comprehensive overview of the state of the art in software resource estimation. We describe common estimation methods and also provide an evaluation framework to systematically compare and assess alternative estimation methods. Although we have tried to be as precise and objective as possible, it is inevitable that such a comparison exercise be somewhat subjective. We however, provide as much information as possible, so that the reader can form his or her own opinion on the methods to employ. We also discuss the applications of such estimation methods and provide practical guidelines. Understanding this article does not require any specific expertise in resource estimation or quantitative modeling. However, certain method descriptions are brief and the level of understanding that can be expected from such a text depends, to a certain extent, on the reader's knowledge. Our objective is to provide the reader with a comprehension of existing software resource estimation methods as well as with the tools to reason about estimation methods and how they relate to the reader's problems. The second section (on resource estimation) briefly describes the problems at hand, the history, and the current status of resource estimation in software engineering research and practice. The third section (on overview of estimation models) provides a comprehensive, although certainly not complete, overview of resource estimation methods. Project sizing, an important issue related to resource estimation, is then discussed in the fourth section (on sizing projects). The fifth section (on framework for com parison and evaluation) defines an evaluation framework that allows us to make systematic and justified comparisons in the sixth section (on evaluation and comparison effort estimation methods). The seventh section (on considerations influencing choice of estimating method) provides guidelines regarding the selection of appropriate estimation methods, in a given context. The eighth section (on typical applications) describes typical scenarios for using resource estimation methods, thereby relating them to software management practice. The ninth section (on future directions) attempts to define important research and practice directions, requiring the collaboration of academia and industry. This article then concludes by summarizing the main points made throughout the article.
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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 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".