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Record W2057837180 · doi:10.1109/iwsm-mensura.2013.26

Towards the Development of a Framework for Education in Software Measurement

2013· article· en· W2057837180 on OpenAlexaff
Mónica Villavicencio, Alain Abran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsÉcole de Technologie Supérieure
FundersDivision of Undergraduate Education
KeywordsComputer scienceProcess (computing)Field (mathematics)Set (abstract data type)SoftwareEducational softwarePersonal software processSoftware developmentSoftware engineeringUndergraduate educationEngineering managementMathematics educationMedical educationSoftware constructionEngineeringPsychology

Abstract

fetched live from OpenAlex

Even though software engineering education embraces the teaching of software measurement topics, there is a lack of guidelines to address this teaching in undergraduate programs. In order to facilitate the education of these topics at the undergraduate level, a set of studies was conducted since 2010 and an educational framework is being developed based on the opinions of experts in the field. The framework provides guidelines for university teachers and instructors to facilitate the teaching and learning process of software measurement for undergraduate students or beginners in the field. The structure of this framework can be applied in other educational fields.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.932
Threshold uncertainty score0.129

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.300
Teacher spread0.248 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

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

Citations4
Published2013
Admission routes1
Has abstractyes

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