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Record W1504156788 · doi:10.1109/icse.2015.203

A Unified Framework for the Comprehension of Software's Time

2015· article· en· W1504156788 on OpenAlexaff
Omar Benomar, Houari Sahraoui, Pierre Poulin

Bibliographic record

Venue2015 IEEE/ACM 37th IEEE International Conference on Software Engineering · 2015
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComputer scienceSoftware evolutionProgram comprehensionDimension (graph theory)ComprehensionSoftware engineeringContext (archaeology)SoftwareSoftware developmentSoftware constructionSoftware systemProgramming languageMathematics

Abstract

fetched live from OpenAlex

The dimension of time in software appears in both program execution and software evolution. Much research has been devoted to the understanding of either program execution or software evolution, but these two research communities have developed tools and solutions exclusively in their respective context. In this paper, we claim that a common comprehension framework should apply to the time dimension of software. We formalize this as a meta-model that we instantiate and apply to the two different comprehension problems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0060.004
Science and technology studies0.0030.017
Scholarly communication0.0120.033
Open science0.0050.007
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0080.002

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 head0.102
GPT teacher head0.341
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

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