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Record W1510651869 · doi:10.1109/iccl.1988.13085

Adapting modules to an integrated programming environment

2003· article· en· W1510651869 on OpenAlex
Nazim H. Madhavji, Josée Desharnais, L. Pinsonneault, K. Toubache

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
TopicAdvanced Software Engineering Methodologies
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceModular designConsistency (knowledge bases)SoftwareModular programmingSoftware engineeringProgramming languageDevelopment environmentArtificial intelligence

Abstract

fetched live from OpenAlex

The design of modules in modular languages, and the model of software development activities portrayed by it, have been well received in batch environments. However, from their experience in the design of the MUPE-2 integrated programming environment, the authors hold the opinion that the model of software activities portrayed by modules in a batch environment is not entirely appropriate in an integrated programming environment. In adapting modules to the MUPE-2 environment, the authors have changed their design and implementation. In particular, they have added a module type, called the supermodule, that can encapsulate related modules, so that the architecture of a software system may be captured, and unified the separate definition and implementation parts of a module into a single module, called the DefImp module, so that some consistency problems due to textual separation can be avoided. They examine the role of modules in a batch environment and give a rationale for their design of modules for integrated environments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.608
Threshold uncertainty score0.403

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 science0.0000.000
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 head0.049
GPT teacher head0.280
Teacher spread0.231 · 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