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Record W2417608523 · doi:10.35426/iav43n113.03

Eficiencia de proyectos de desarrollo de software y modelos de conversión de funcionalidad

2013· article· es· W2417608523 on OpenAlexaff
Ricardo Chávez Arellano, Daniel Pineda Domínguez, Juan J. Cuadrado‐Gallego

Bibliographic record

VenueInvestigación Administrativa · 2013
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsUniversité du Québec
Fundersnot available
KeywordsHumanitiesAgile software developmentPhilosophyComputer scienceSoftware engineering

Abstract

fetched live from OpenAlex

La industria del software es fundamental en el funcionamiento de la sociedad y las organizaciones de nuestro tiempo. Éstas invierten en sistemas de información para su administración de manera automatizada, lo cual requiere un proyecto de desarrollo de sistemas que se produce en la industria del software bajo el nuevo paradigma conocido como Agile. El objetivo de esta investigación fue desarrollar un modelo de conversión entre el método común de medición de funcionalidad de software de Puntos de función de IFPUG y el de Puntos de relato de Agile, obteniéndose nueve modelos de conversión entre ambas metodologías y cuyo uso ayudará a ser más eficientes los proyectos de desarrollo de software.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.037
GPT teacher head0.235
Teacher spread0.199 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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