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Record W2092358306 · doi:10.1145/2577554.2577559

Genealogical insights into the facts and fictions of clone removal

2013· article· en· W2092358306 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

VenueACM SIGAPP Applied Computing Review · 2013
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
Keywordsclone (Java method)Software maintenanceEmpirical researchComputer scienceSoftwareData scienceSoftware systemBiologyProgramming languageGeneticsStatisticsGeneMathematics

Abstract

fetched live from OpenAlex

Clone management has drawn immense interest from the research community in recent years. It is recognized that a deep understanding of how code clones change and are refactored is necessary for devising effective clone management tools and techniques. This paper presents an empirical study based on the clone genealogies from a significant number of releases of nine software systems, to characterize the patterns of clone change and removal in evolving software systems. With a blend of qualitative analysis, quantitative analysis and statistical tests of significance, we address a number of research questions. Our findings reveal insights into the removal of individual clone fragments and provide empirical evidence in support of conventional clone evolution wisdom. The results can be used to devise informed clone management tools and techniques.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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