MétaCan
Menu
Back to cohort
Record W2007780858 · doi:10.1145/2024587.2024592

Do software process improvements lead to ISO 9126 architectural quality factor improvement

2011· article· en· W2007780858 on OpenAlexaff
Mathieu Lavallée, Pierre N. Robillard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsPolytechnique Montréal
FundersDivision of Civil, Mechanical and Manufacturing Innovation
KeywordsCapability Maturity Model IntegrationQuality (philosophy)Software qualityProcess (computing)Computer scienceSoftware quality controlSystems engineeringSoftware development processEngineeringProcess managementReliability engineeringSoftware engineeringSoftwareRisk analysis (engineering)Software developmentBusiness

Abstract

fetched live from OpenAlex

This paper presents preliminary results of a systematic review performed to determine the impacts of Software Process Improvements (SPI) on developers' activities and on architectural quality. The analysis shows that most SPI research focuses on the motivations of developers like quality of work life and participation incentives, but provides little detail on the impacts of SPI on their day-to-day tasks. The impacts on product quality are limited to defect reduction, and do not consider architectural quality factors, such as changeability and stability. This study shows a very weak link between process quality, as defined by the CMMI, and architectural quality, as defined by ISO 9126. The SPI literature found by this review is mostly concerned with requirement process improvements, which are related to problem definition quality, but not to architectural quality. Future quality-oriented SPI research should therefore focus on improving design and development processes with an eye to considering architectural quality factors, or what the ISO 9126 terms "architectural capabilities".

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.058
GPT teacher head0.326
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

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
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicSoftware Engineering Techniques and PracticesFrench-language works237,207