MétaCan
Menu
Back to cohort

Implementing systems engineering and project management processes in a Canadian company – Overview and Results Achieved

2019· article· en· W2975056157 on OpenAlexaffabout
Nicolas Tremblay, Claude Y. Laporte, Denis Poliquin, Jamil Menaceur

Bibliographic record

VenueINCOSE International Symposium · 2019
Typearticle
Languageen
FieldComputer Science
TopicSoftware Reliability and Analysis Research
Canadian institutionsLachine HospitalÉcole de Technologie Supérieure
Fundersnot available
KeywordsCapability Maturity Model IntegrationMaturity (psychological)AuditProcess (computing)Engineering managementEngineeringSystems engineeringInformation security management systemComputer scienceSoftware development processSoftware developmentSoftwareBusiness

Abstract

fetched live from OpenAlex

Abstract This article presents a project consisting of implementing project management and systems engineering processes at CSinTrans Inc. (CSiT), a Canadian company founded in 2011. CSiT provides multi‐modal transit information systems as well as information integration to the transit industry worldwide. The Basic profile of the ISO/IEC 29110 for systems engineering has been used as the main reference for the development of these processes. The reasons that prompted CSiT to implement the ISO/IEC 29110 are mentioned. The approach and details on how the standard has been implemented are presented. The lessons learned are described. CSiT developed three process groups to match the attributes of projects such as size and nature. The selection of tools to support the processes is discussed. Third‐party audits, conducted annually since 2016, that led CSiT to become the first systems engineering company successfully audited with ISO/IEC 29110, are presented as well as the benefits obtained. ISO/IEC 29110 has helped raise the maturity of the organization by implementing proven practices and developing consistent work products from one project to another. ISO/IEC 29110 was a good starting point to align processes with specific practices of CMMI® Maturity Levels 2 and 3. ISO/IEC 29110 has also helped CSiT in developing light processes as well as remaining flexible and quick in its ability to respond to its customers. To illustrate the implementation of the Basic profile of ISO/IEC 29110 in other engineering domains, this article briefly presents the implementation in the automotive, agriculture, aeronautic, nuclear and space domains in 6 enterprises of France in 2018.

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)0.014
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.644

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0010.001
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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueINCOSE International SymposiumSame topicSoftware Reliability and Analysis ResearchFrench-language works237,207