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Record W2120167743 · doi:10.1109/icsm.2007.4362634

Polylingual Dependency Analysis Using Island Grammars: A Cost Versus Accuracy Evaluation

2007· article· en· W2120167743 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

VenueProceedings/Proceedings - Conference on Software Maintenance · 2007
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDependency (UML)Rule-based machine translationComputer scienceNatural language processingL-attributed grammarArtificial intelligenceProgramming languageContext-free grammar

Abstract

fetched live from OpenAlex

Software dependency analysis is an important step in determining the potential impact of changes. Existing tool support for conducting dependency analysis does not sufficiently support systems written in more than one language. Tools based on semantic analyses are expensive to create for combinations of multiple languages, while lexical tools provide poor accuracy and rely heavily on developer skill. This paper reports on an investigation into the application of a series of incrementally-better island grammars to an industrial, open-source polylingual system to determine the cost-to-accuracy relationship involved in developing and applying island grammars for dependency analysis. The results of our study suggest the effort-cost in writing richer island grammars rises faster than the resulting accuracy.

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)0.005
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0000.001
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 head0.072
GPT teacher head0.348
Teacher spread0.276 · 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