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Record W2127619534 · doi:10.1109/wcre.1998.723173

Evaluating architectural extractors

2002· article· en· W2127619534 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
TopicSoftware Engineering Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWorkbenchComputer scienceSoftware engineeringReverse engineeringSoftwareVisualizationSource codeArchitectural patternSoftware systemSoftware constructionProgramming languageArtificial intelligence

Abstract

fetched live from OpenAlex

One of the goals of reverse engineering a software system is to extract an architectural design from the source code. This paper compares a selection of tools available to perform this architectural recovery. The following tools are examined: Rigi (Muller, 1996), the Dali workbench (Kazman and Carriere, 1998), the Software Bookshelf (PBS) (Finnigan et al., 1997), CIA (Chen et al., 1990) and SNiFF+. This comparison is based on the abilities of the tools to perform data extraction, classification, and visualization. Of the tools evaluated, the Software Bookshelf and the Dali workbench were found to be the most suitable for architectural recovery.

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)0.001
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: none
Teacher disagreement score0.979
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.099
GPT teacher head0.345
Teacher spread0.246 · 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

Quick stats

Citations57
Published2002
Admission routes1
Has abstractyes

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