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Record W2099706779 · doi:10.1109/icsm.2009.5306335

Searching and skimming: An exploratory study

2009· article· en· W2099706779 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 Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceFormative assessmentTask (project management)Source codeProgram comprehensionExploratory researchCode (set theory)Task analysisSoftwareCode reviewHuman–computer interactionSoftware engineeringStatic program analysisSoftware developmentWorld Wide WebData scienceSoftware systemProgramming languageSet (abstract data type)Engineering

Abstract

fetched live from OpenAlex

Source code search is an important activity for programmers working on a change task to a software system. As part of a larger project to improve tool support for finding information in source code, we conducted a formative study in which programmers were asked to perform corrective tasks to a system they were initially unfamiliar with. Our analysis focused specifically on how programmers decide what to search for, and how they decide which results are relevant to their task. Based on our analysis, we present five observations about our participant's approach to finding information and some of the challenges they faced. We also discuss the implications these observations have for the design of source code search tools.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.310
Teacher spread0.276 · 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

Quick stats

Citations75
Published2009
Admission routes1
Has abstractyes

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