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Record W4247334800 · doi:10.1109/icse.1988.93690

Rigi: a system for programming-in-the-large

2003· article· en· W4247334800 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Victoria
FundersUniversity of VictoriaRice University
KeywordsComputer scienceConsistency (knowledge bases)TraceabilityAbstractionTheoretical computer scienceGraphProgramming languageSoftwareGeneralizationSoftware systemSoftware developmentSoftware engineeringArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

The authors describe Rigi, a model and tool that uses a graph model and abstraction mechanisms to structure and represent the information accumulated during the development process. The objects and relationships of the graph model represent system components and their dependencies. The objects can be arranged in aggregation and generalization hierarchies. Rigi was designed to address three of the most difficult problems in the area of programming-in-the-large: the mastery of the structural complexity of large software systems, the effective presentation of development information, and the definition of procedures for checking and maintaining the completeness, consistency, and traceability of system descriptions. Thus, the major objective of Rigi is to effectively represent and manipulate the building blocks of a software system and their myriad dependencies, thereby aiding the development phases of the project.< <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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.919
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.041
GPT teacher head0.304
Teacher spread0.262 · 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