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Record W2158949715 · doi:10.1002/0471028959.sof282

Resource Estimation in Software Engineering

2002· other· en· W2158949715 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEncyclopedia of Software Engineering · 2002
Typeother
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsCarleton University
Fundersnot available
KeywordsEstimationComputer scienceResource (disambiguation)SizingSoftwareCost estimateManagement scienceOperations researchData scienceSoftware engineeringIndustrial engineeringData miningSystems engineeringEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.609
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.214
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it