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Record W2036164091 · doi:10.1109/re.2007.55

Viewing Project Collaborators WhoWork on Interrelated Requirements

2007· article· en· W2036164091 on OpenAlexaff
Irwin Kwan, Sabrina Marczak, Daniela Damian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsRequirements analysisComputer scienceRequirements engineeringRequirements managementRequirement prioritizationSet (abstract data type)Plan (archaeology)Non-functional requirementListing (finance)InterdependenceWork breakdown structureWork (physics)Project managementSoftware requirements specificationVisualizationSoftware project managementProject teamProject management triangleSoftwareSoftware developmentSystems engineeringKnowledge managementEngineeringProject charterSoftware design

Abstract

fetched live from OpenAlex

Project collaborators in a software development project need to stay aware not only of changes to requirements and other artifacts, but also of each other's current work. The set of team members working on a requirement is dynamic, and team members who were not assigned to the requirement in the plan may be involved. If this requirement changes, those team members who are dependent on that requirement must be notified quickly before they do outdated work. However, project plans often do not provide an easy method of listing all of the emergent team members who should be notified of changes to a requirement. We propose a requirements-dependency diagram that displays interdependent requirements and team members who are assigned to these interdependent requirements. The visualization highlights prominent collaborators, lists each collaborator and each requirement only once, marks emergent collaborators, and is simple and clutter-free. By viewing this diagram, collaborators will know who to contact to notify others of changes to requirements, and can contact experts working on interrelated requirements.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.659
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.045
GPT teacher head0.337
Teacher spread0.292 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2007
Admission routes1
Has abstractyes

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