MétaCan
Menu
Back to cohort
Record W1897360739 · doi:10.1109/ccece.2004.1345092

Development of an intelligent system for architecture design and analysis [software architecture]

2004· article· en· W1897360739 on OpenAlexaff
Jingqiu Shao, Behrouz H. Far

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceSoftware engineeringSoftware architectureReference architectureArchitectural patternArchitectureUSableSoftware architecture descriptionApplications architectureAsset (computer security)Resource-oriented architectureProcess (computing)Software systemSoftware developmentSystems engineeringSoftwareSoftware designEngineeringWorld Wide WebComputer securityOperating system

Abstract

fetched live from OpenAlex

Software architecture plays a pivotal role in allowing an organization to meet its business goals, in terms of the early insights it provides into the system, the communication it enables among stakeholders, and the value it provides as a re-usable asset. Unfortunately, designing and analyzing architecture for a certain system is recognized as a hard task for most software engineers, because the process of collecting, maintaining, and validating architectural information is complex, knowledge-intensive, iterative, and error prone. The needs of software architectural design and analysis have led to a desire to create tools to support the process. This paper introduces an intelligent system, which serves the following purposes: to obtain meaningful nonfunctional requirements from users; to aid in exploring architectural alternatives; and to facilitate architectural analysis.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.823
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.045
GPT teacher head0.288
Teacher spread0.243 · 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 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
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Software Engineering MethodologiesFrench-language works237,207