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Analysis of the Evolution of Eight VSEs Using the ISO/IEC 29110 to Reinforce Their Agile Approaches

2020· book-chapter· en· W3045412379 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in systems analysis, software engineering, and high performance computing book series · 2020
Typebook-chapter
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAgile software developmentEngineeringSoftwareSoftware engineeringLean software developmentAgile Unified ProcessEngineering managementSystems engineeringProcess managementSoftware developmentSoftware development processComputer science

Abstract

fetched live from OpenAlex

Most very small entities (VSEs) develop software for medium and large companies and organizations. This situation creates an opportunity for them to become key players in the production chain by providing quality software within schedule and budget. A feature of most VSEs is that they do not have experience in the implementation of engineering standards due to specific features such as lack of support, lack of resources, time-consuming, and the use of agile approaches. This chapter presents an analysis of a set of eight VSEs that used agile approaches to develop software and that have implemented the software Basic profile of the ISO/IEC 29110 to reinforce their agile approach. The results show that ISO/IEC 29110 were easily implemented and helped VSEs to improve their agile approaches while helping them to understand the importance of formalizing some key artifacts produced during the development of a software product.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.014
GPT teacher head0.212
Teacher spread0.198 · 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