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Record W2113207560 · doi:10.1109/hicss.1997.663177

Use case maps for engineering real time and distributed computer systems: a case study of an ACE-framework application

2002· article· en· W2113207560 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 institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceSoftware engineeringBusiness process reengineeringTraceabilityCode (set theory)Software systemObject-oriented programmingSource codeDistributed computingSoftwareProgramming languageEngineeringSet (abstract data type)

Abstract

fetched live from OpenAlex

Two major problems in the engineering of software-intensive, real time and distributed computer systems are, without becoming lost in code details: understanding how an implemented system works as a whole; and specifying, before implementation starts, how the required behaviour of the whole system is to be achieved. These lead to other problems, such as, long iteration cycles during forward engineering while various code changes are tried in attempts to fix erroneous system behaviour, and inadvertently introducing code changes during maintenance or reengineering that will damage correct system behaviour because there is a lack of backwards traceability to it from the code. This paper illustrates the application of a new technique called use case maps to solving these problems, using as an example a system constructed from a public domain, object-oriented software framework called ACE.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.083
Threshold uncertainty score0.616

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 head0.053
GPT teacher head0.290
Teacher spread0.237 · 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