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Managing Knowledge in Open Source Software Test Process

2013· book-chapter· en· W101575415 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 · 2013
Typebook-chapter
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsConcordia University
Fundersnot available
KeywordsConceptual modelProcess (computing)Knowledge managementSoftware engineeringSoftware development processComputer scienceSoftware developmentEngineeringProcess managementSoftwareProgramming language

Abstract

fetched live from OpenAlex

The increasing adoption and use of Open Source Software (OSS) motivates study of its development. This chapter explores the state-of-the art in OSS development processes, in general, and OSS testing processes, in particular. A conceptual model for software Testing Knowledge Management (TKM) that aims to provide an understanding of the testing domain is introduced. The TKM model is informed by earlier studies and guided by international testing standards. Moreover, the TKM model is equipped with different forms of knowledge, reusable across software projects. Using the TKM model as an integrative conceptual model enables understanding of how knowledge life cycle stages are mapped onto the test process of OSS, what type of knowledge is created at each stage, and how knowledge is converted from one stage to another. The chapter is supported by representative examples of OSS that are mature and currently in widespread use.

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), Scholarly communication
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 score0.770
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.006
Open science0.0030.002
Research integrity0.0000.001
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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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