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Record W1622033010 · doi:10.1109/agile.2015.16

Understanding Digital Cardwall Usage

2015· article· en· W1622033010 on OpenAlexafffund
Stevenson Gossage, Judith M. Brown, Robert Biddle

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAgile software developmentComputer scienceUser storyKey (lock)Process (computing)Work (physics)FidelityField (mathematics)SoftwareWorld Wide WebSoftware developmentSoftware engineeringEngineeringComputer security

Abstract

fetched live from OpenAlex

In Agile software development, key artefacts used to support the process are the User Story (usually recorded on a Storycard) and Story Cardwall (usually a dedicated portion of a wall). These low-fidelity tools work together to help teams stay focused and self-manage their projects. The need to support distributed teams and team members makes the physical Cardwall impractical and teams are therefore migrating towards digital story management tools. We conducted field studies of 8 Agile teams using digital Cardwalls, and performed qualitative data analysis to understand patterns of usages and user needs. We identify issues to address in the design of digital Cardwalls.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.875
Threshold uncertainty score0.311

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 communication0.0000.001
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 head0.219
GPT teacher head0.284
Teacher spread0.065 · 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 designTheoretical or conceptual
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

Citations9
Published2015
Admission routes2
Has abstractyes

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