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Record W2036487649 · doi:10.1109/ms.2012.24

Contemporary Peer Review in Action: Lessons from Open Source Development

2012· article· en· W2036487649 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

VenueIEEE Software · 2012
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsDefence Research and Development CanadaUniversity of VictoriaConcordia University
FundersEngineering and Physical Sciences Research Council
KeywordsAgile software developmentCode reviewSoftware engineeringComputer scienceSoftware developmentSoftware qualitySoftware inspectionSoftware development processSoftware peer reviewPersonal software processProcess (computing)Technical peer reviewTeam software processAsynchronous communicationSoftware technical reviewSoftwareProcess managementEngineeringSoftware constructionPeer reviewOperating system

Abstract

fetched live from OpenAlex

Do you use software peer reviews? Are you happy with your current code review practices? Even though formal inspection is recognized as one of the most effective ways to improve software quality, many software organizations struggle to effectively implement a formal inspection regime. Open source projects use an agile peer review process-based on asynchronous, frequent, incremental reviews that are carried out by invested codevelopers-that contrasts with heavyweight inspection processes. The authors describe lessons from the OSS process that transfer to proprietary software development. They also present a selection of popular tools that support lightweight, collaborative, code review processes and nonintrusive metric collection.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.170
GPT teacher head0.379
Teacher spread0.210 · 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