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Record W2101880321 · doi:10.1109/chase.2009.5071404

Audio-video recording of ad hoc software development team interactions

2009· article· en· W2101880321 on OpenAlexaff
Sébastien Cherry, Pierre N. Robillard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTeamworkComputer scienceSoftwareSoftware developmentMultimediaProcess (computing)Team software processFace (sociological concept)Post hocSoftware development processHuman–computer interaction

Abstract

fetched live from OpenAlex

Ad hoc interactions characterize the natural behaviors observed in any teamwork situation. Our objective is to find out how these interactions are related to the roles team mates play. Observations on the phenomenon are based on audio-video recordings of software development team interactions occurring in a large software development organization with a highly standardized software development process in place. Detailed observations were recorded on the activities of four of the twelve members of the development team. Thirty-five hours of audio-video recordings were analyzed, in which a total of 404 face-to-face ad hoc collaborative interactions were observed. The results provided a quantitative demonstration of the impact of a newcomer, a ldquogururdquo, and a project manager on the team dynamics. This study, although conducted in a specific professional environment, can provide useful information for a better understanding of the face-to-face communication needs of a software development team.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.273
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2009
Admission routes1
Has abstractyes

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