MétaCan
Menu
Back to cohort
Record W2547608718 · doi:10.1109/hcc.2003.1260235

Cognitive wallithrough + personae = an empirical infrastructure for modeling software developers

2004· article· en· W2547608718 on OpenAlexaff
Rozita Naghshin, Ahmed Seffah, Rex B. Kline

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsConcordia University
Fundersnot available
KeywordsUsabilityComputer scienceLearnabilityHeuristic evaluationHuman–computer interactionSoftware engineeringSoftwareEmpirical researchCognitionProgramming languagePsychology

Abstract

fetched live from OpenAlex

This is the third in a series of reports about usability and learnability problems of integrated development environments (IDE). The first report used the method of ethnographic interviews, and the second report the methods of heuristic and psychometric evaluation to study problems that developers face in using IDEs. The present study extends previous work by applying the method of direct behavioral observation of more versus less experienced users of the same IDE for C++. We demonstrate in this paper how one can effectively use cognitive walkthroughs and code walkthroughs for empirically modeling developers' characteristics and personae.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.464
Threshold uncertainty score0.570

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 communication0.0000.001
Open science0.0010.000
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 head0.333
Teacher spread0.286 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicSoftware Engineering Techniques and PracticesFrench-language works237,207