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Record W1607839285 · doi:10.1109/ase.2004.58

Refactoring use case models on episodes

2004· article· en· W1607839285 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

VenueSpectrum Research Repository (Concordia University) · 2004
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsCode refactoringComputer scienceSoftware engineeringProperty (philosophy)Programming languageSoftware

Abstract

fetched live from OpenAlex

Use case models are used to capture functionality requirements of a system. Use cases can be described in term of the episodes, or subtasks, that are performed. An episode model captures the organization and relationships of the episodes in a use case. Refactoring is an approach to reorganize the internal structure of models in order to improve them or extend them in some way. Refactorings are behavior-preserving transformations of the models. This thesis looks at refactoring of use case models based on the information captured in episode models. We present a metamodel for the use case and the episode model in order to make precise the syntax of the models and explain the informal semantics of the models. We detail several refactoring rules for use case refactoring, including their verification of the behavior-preserving property. We also present a case study based on the Video Store System.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.001
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 head0.063
GPT teacher head0.281
Teacher spread0.218 · 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