MétaCan
Menu
Back to cohort
Record W2144961539 · doi:10.1109/icpc.2011.35

The Influence of the Task on Programmer Behaviour

2011· article· en· W2144961539 on OpenAlexafffund
Annie T. T. Ying, Martin P. Robillard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsComputer scienceProgrammerSession (web analytics)Task (project management)SurpriseTRACE (psycholinguistics)Programming languageSource codeSoftware developmentSoftwareWorld Wide Web

Abstract

fetched live from OpenAlex

Programmers performing a change task must understand the existing software in addition to performing the actual change. This process is likely to be affected by characteristics of the task. We investigated whether the nature of a task has any relationship with when a programmer edits code during a programming session. We characterized differences in editing behaviour with three types of editing styles: edit-first, edit-last, and edit-throughout. We based our analysis on the interaction history of over 4000 programming sessions collected as part of the development history of open source projects. Our results showed that an enhancement task (as opposed to a bug fix) was less likely to be associated with a high fraction of source code edit events at the beginning of the programming session. To our surprise, we also found that the presence of a stack trace in a bug report did not significantly effect the editing style of the programming session.

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)0.008
metaresearch head score (Gemma)0.154
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.154
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

Explore more

Same topicSoftware Engineering ResearchFrench-language works237,207