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Record W2096287423 · doi:10.1109/icse.2009.5070504

How tagging helps bridge the gap between social and technical aspects in software development

2009· article· en· W2096287423 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBridge (graph theory)Computer scienceMechanism (biology)Empirical researchKnowledge managementSoftwareSoftware developmentDomain (mathematical analysis)Collaborative softwareWork (physics)Software engineeringData scienceEngineering

Abstract

fetched live from OpenAlex

Empirical research on collaborative software development practices indicates that technical and social aspects of software development are often intertwined. The processes followed are tacit and constantly evolving, thus not all of them are amenable to formal tool support. In this paper, we explore how ldquotaggingrdquo, a lightweight social computing mechanism, is used to bridge the gap between technical and social aspects of managing work items. We present the results from an empirical study on how tagging has been adopted and adapted over the past two years of a large project with 175 developers. Our research shows that the tagging mechanism was eagerly adopted by the team, and that it has become a significant part of many informal processes. Our findings indicate that lightweight informal tool support, prevalent in the social computing domain, may play an important role in improving team-based software development practices.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.322

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 communication0.0000.000
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 head0.047
GPT teacher head0.285
Teacher spread0.238 · 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