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Record W816408658

Mise En Scene: Conversion of Scenarios to CSP Traces for the Requirements-To-Design-To-Code Project

2013· book· en· W816408658 on OpenAlexaff
Carter. John D., William B. Gardner, James L. Rash, Mike Hinchey

Bibliographic record

VenueNASA Technical Reports Server (NASA) · 2013
Typebook
Languageen
FieldComputer Science
TopicAdvanced Software Engineering Methodologies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNotationComputer scienceTRACE (psycholinguistics)Programming languageCommunicating sequential processesCode (set theory)ScheduleSoftware engineeringSemantics (computer science)Operating systemSet (abstract data type)
DOInot available

Abstract

fetched live from OpenAlex

The Requirements-to-Design-to-Code (R2D2C) project at NASA's Goddard Space Flight Center is based on deriving a formal specification expressed in Communicating Sequential Processes (CSP) notation from system requirements supplied in the form of CSP traces. The traces, in turn, are to be extracted from scenarios, a user-friendly medium often used to describe the required behavior of computer systems under development. This work, called Mise en Scene, defines a new medium (Scenario Notation Language, SNL) suitable for control-dominated systems, coupled with a two-stage process for automatic translation of scenarios to a new trace medium (Trace Notation Language, TNL) that encompasses CSP traces. Mise en Scene is offered as an initial solution to the problem of the scenarios-to-traces D2 phase of R2D2C. A survey of the scenario concept and some case studies are also provided.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.027
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.013

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 head0.088
GPT teacher head0.343
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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