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Record W2143082089 · doi:10.1145/581339.581383

Architecture recovery of web applications

2002· article· en· W2143082089 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceWeb modelingWeb engineeringArchitectureWeb applicationSoftware engineeringSoftware architectureData WebWorld Wide WebWeb serviceLegacy systemProcess (computing)SoftwareWeb intelligenceOperating system

Abstract

fetched live from OpenAlex

Web applications are the legacy software of the future. Developed under tight schedules, with high employee turn over, and in a rapidly evolving environment, these systems are often poorly structured and poorly documented. Maintaining such systems is problematic.This paper presents an approach to recover the architecture of such systems, in order to make maintenance more manageable. Our lightweight approach is flexible and retargetable to the various technologies that are used in developing web applications. The approach extracts the structure of dynamic web applications and shows the interaction between their various components such as databases, distributed objects, and web pages. The recovery process uses a set of specialized extractors to analyze the source code and binaries of web applications. The extracted data is manipulated to reduce the complexity of the architectural diagrams. Developers can use the extracted architecture to gain a better understanding of web applications and to assist in their maintenance.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.823
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.200
Teacher spread0.191 · 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