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Component-Based Modeling for Information Systems Reengineering

2012· book-chapter· en· W1450648329 on OpenAlexaff
Malleswara Talla, Raul Valverde

Bibliographic record

VenueIGI Global eBooks · 2012
Typebook-chapter
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsComponent (thermodynamics)Business process reengineeringComputer scienceInterdependenceInformation systemSystems engineeringDomain (mathematical analysis)Business processContext (archaeology)Business requirementsSoftware engineeringComponent-based software engineeringBusiness process modelingSoftware systemEngineeringSoftwareManufacturing engineeringWork in process

Abstract

fetched live from OpenAlex

An Information System can be envisioned as a set of interdependent components that provide the intended services. The component based modeling serves as a tool for collecting requirements of an Information System in user perspective and business perspective at various stages of software development. The chapter presents a methodology for component based modeling and development of an Information System, starting from the requirements definition phase, arriving at candidate components and creation of final components and their interfaces. The methodology aims at clarifying the intricate details and usage of an Information System via business type models and use-case models. The chapter presents the interaction diagrams in order to describe interactions among objects in systems perspective, and context diagrams for reflecting upon the business domain. Finally, the chapter proposes component replacement as a methodology for system reengineering, and model-view-control framework for component refinement and evolution in order to achieve a reengineered information system that reflects upon current requirements in business domain. The reengineering techniques proposed in this chapter can be applied to legacy systems to turn them into a component-oriented reengineered 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.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.050
GPT teacher head0.264
Teacher spread0.214 · 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.

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
Published2012
Admission routes1
Has abstractyes

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