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Record W6901837174 · doi:10.60692/t1shc-fz308

Beyond factors that motivate the adoption of the ISO/IEC 29110 in Mexico: An exploratory study of the implementation pace of this standard and the benefits observed

2021· article· en· W6901837174 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2021
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsPaceExploratory researchSoftwareSoftware developmentSoftware development processSchedule

Abstract

fetched live from OpenAlex

Around the world, the importance of Very Small Entities (VSEs), organisations having up to 25 people, has been constantly increasing. Worldwide, VSEs represent over 92% of the software industry. Therefore, two main needs can be highlighted: a) providing VSEs with resources to produce high-quality software, and b) training software engineering undergraduates in proven practices to produce high-quality software within the given schedule and budget. A logical way to meet these needs is transfering proven practices provided by software engineering standards. However, transferring the knowledge of software engineering standards is not always an easy task. The ISO/IEC 29110 is a series of international standards and guides that provide codified knowledge related to the software development process. The series was specifically developed to be used by VSEs. This study presents an exploratory analysis, conducted in 12 Mexican VSEs, which implemented the software Basic profile of the ISO/IEC 29110, to identify the pace at which they can adopt this standard to their environment. Besides, the benefits and difficulties encountered are provided. The results can be relevant for other VSEs interested in implementing this standard. Even if the exploratory analysis was performed in the VSEs of Mexico, this analysis can be of interest in other countries. The results obtained can help other VSEs that are interested in the adoption of this international standard to reduce the barriers to a successful implementation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.248
Teacher spread0.191 · 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 teacher head, 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

Citations0
Published2021
Admission routes1
Has abstractyes

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