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Record W2614528296 · doi:10.1590/s0034-759020170206

SOFTWARE PROCESS IMPROVEMENT: AWARENESS, USE, AND BENEFITS IN CANADIAN SOFTWARE DEVELOPMENT FIRMS

2017· article· en· W2614528296 on OpenAlexaboutno aff
Delroy A. Chevers

Bibliographic record

VenueRevista de Administração de Empresas · 2017
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware developmentTeam software processSoftwareSoftware quality controlSoftware development processQuality (philosophy)Personal software processProcess (computing)Product (mathematics)Sample (material)Process managementSoftware qualityBusinessComputer scienceSoftware constructionOperating system

Abstract

fetched live from OpenAlex

ABSTRACT Since 1982, the software development community has been concerned with the delivery of quality systems. Software process improvement (SPI) is an initiative to avoid the delivery of low quality systems. However, the awareness and adoption of SPI is low. Thus, this study examines the rate of awareness, use, and benefits of SPI initiatives in Canadian software development firms. Using SPSS as the analytical tool, this study found that 59% of Canadian software development firms are aware of SPI programs and 43% of employees use a form of SPI programs to develop software products. Although the sample size is small and the results cannot be generalized, the sample firms that use SPI programs reported an improvement in software product quality as the greatest benefit. These findings confirm the importance of SPI programs as a means of producing higher-quality software products, which can increase the likelihood of software companies winning global contracts.

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.301
Teacher spread0.262 · 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.

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

Citations6
Published2017
Admission routes1
Has abstractyes

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