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Record W84714789

Architecture-Centric Program Transformation for Distributed Systems.

2006· article· en· W84714789 on OpenAlexaff
Chung–Horng Lung, Jianning Liu, Xiaoli Ling, Dan Jiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceDatabase-centric architectureSoftware architectureSoftware evolutionReference architectureSoftware engineeringArchitectureArchitecture tradeoff analysis methodResource-oriented architectureTransformation (genetics)Applications architectureSoftwareSoftware systemComponent-based software engineeringSoftware constructionProgramming language
DOInot available

Abstract

fetched live from OpenAlex

Software evolution is inevitable due to changes in requirements and technology. Software quality and productivity are critical to software evolution. Architecture-centric evolution has the advantage of improving quality attributes; however, it does not directly address the issue of the time needed for evolution. This paper proposes an architecture-centric program transformation approach to support both quality and productivity concerns. The approach emphasizes the analysis of similarities and differences among architecture alternatives, which facilities the tool development for architecture evolution or evaluation. A tool was constructed to automatically transform a program to another form based on user’s selection. The tool currently supports the transformation of one traditional design to the other two commonly used architecture patterns in distributed and concurrent systems. 1.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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 designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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