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From an Ad-Hoc to a Proactive Software Release Plan and Deployment

2012· book-chapter· en· W2506113519 on OpenAlexaff
Silvia Ito, Mohammed Nahhas

Bibliographic record

VenueIGI Global eBooks · 2012
Typebook-chapter
Languageen
FieldComputer Science
TopicSoftware Reliability and Analysis Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsSoftware deploymentUpgradeSoftwareAerospaceSocial software engineeringSoftware developmentComputer scienceEngineeringProcess managementEngineering managementSystems engineeringSoftware engineeringSoftware construction

Abstract

fetched live from OpenAlex

Managing software and its various components such as maintenance, upgrades, and patches, has become a requirement to secure one’s place in a competitive market. The rapid change in engineering software, especially for the Aerospace industry, highlights the importance of software management. Negligence of any maintenance component can put the organization into risk. Therefore, standardizing processes are necessary to avoid not only these risks, but also minimize costs and ensure employee productivity. Currently, an Aerospace company upgrades most of their engineering software on an ad-hoc basis. This means that an upgrade is only done when an end-user requests it. The study will therefore examine the current operations and offer a strategic reengineered process. The objective of such changes is to ultimately streamline the distribution of engineering software upgrades proactively throughout the organization.

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)0.002
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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.006

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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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
Published2012
Admission routes1
Has abstractyes

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