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8.2.2 Improvement of Software Engineering Performances An Experience Report at Bombardier Transportation – Total Transit Systems Signalling Group

2007· article· en· W2045085509 on OpenAlexaff
Claude Y. Laporte, Denis Roy, Mikel Doucet, Marc Drolet

Bibliographic record

VenueINCOSE International Symposium · 2007
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsBombardier (Canada)Computer Research Institute of MontréalÉcole de Technologie Supérieure
FundersAustralian Government
KeywordsBaseline (sea)Process managementProcess (computing)Maturity (psychological)Engineering managementKey (lock)Transport engineeringComputer scienceOperations managementEngineeringPsychologyOperating systemPolitical science

Abstract

fetched live from OpenAlex

Abstract The performance of the Bombardier TTS/Pittsburgh Signalling group has been evaluated twice: first in November 2003 and again in January 2006. The 2003 evaluation established a baseline for the evaluation of progress made in 2006. During those visits, the same evaluation method was used to evaluate project performance and organizational change management, i.e. the people issues. Since 2003, there has been substantial improvement in both process maturity level and process performance. This experience report, at Bombardier Transportation, illustrates that process performance improvements are achievable when two key factors are involved, namely: a link between business goals and process improvement activities, and a sponsor committing the right level of resources to the improvement program. This paper explains the multi‐dimensional methodology used to perform the evaluations, as well as the business goals and the quantitative performance improvements achieved since 2003.

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)0.009
metaresearch head score (Gemma)0.009
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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
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

Citations1
Published2007
Admission routes1
Has abstractyes

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