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Record W2054942051 · doi:10.4236/jsea.2011.411074

Extending Extreme Programming User Stories to Meet ISO 9001 Formality Requirements

2011· article· en· W2054942051 on OpenAlexaff
Malik Qasaimeh, Alain Abran

Bibliographic record

VenueJournal of Software Engineering and Applications · 2011
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsUser storyCertificationAgile software developmentCapability Maturity Model IntegrationSoftware engineeringComputer scienceUser requirements documentFormalityProcess managementIdentification (biology)SoftwareEngineeringSoftware development processSoftware development

Abstract

fetched live from OpenAlex

For software organizations needing ISO 9001 certification, including those that have adopted agile methodologies, it is important that their software life cycle processes be able to manage the requirements imposed by this certification standard. However, the user stories in the XP agile methodology do not provide auditors with enough evidence that certain steps and activities have been performed in compliance with ISO 9001. This paper proposes an extension to the user story, based on four sub processes related to the CMMI-DEV model: 1) identification of the source of the user story; 2) categorization of the non functional requirements; 3) identification of the user story relationships; and 4) prioritization of the user stories. These sub processes are aligned with the XP release planning phase, and enhance the ability of user stories to accumulate the information that is mandatory for achieving ISO 9001 certification.

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)0.012
metaresearch head score (Gemma)0.044
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 score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.007
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.280
Teacher spread0.227 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

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