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Record W2300062158 · doi:10.11575/prism/30915

An Exploratory Study of Automated GUI Testing: Goals, Issues, and Best Practices

2014· article· en· W2300062158 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

VenueOpen MIND · 2014
Typearticle
Languageen
FieldComputer Science
TopicSoftware Testing and Debugging Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTest suiteSuiteComputer scienceGraphical user interface testingSoftware engineeringKeyword-driven testingBest practiceTest (biology)Test strategyGraphical user interfaceHuman–computer interactionTest caseProgramming languageUser experience designMachine learningSoftwareSoftware developmentSoftware construction

Abstract

fetched live from OpenAlex

Manually testing GUIs can be expensive and complex, so the creation of automated GUI test suites has been an area of significant interest. However, to our knowledge, the motivations of testers and the problems they encounter when attempting to create and use automated GUI tests have not been explored. We used Grounded Theory to investigate the goals motivating automated GUI testing, the issues testers encounter, and the best practices applied to overcome these issues. Through this study, we demonstrate that automated GUI test suite evolution and architecture are extremely important to the success of automated GUI testing and describe techniques that can be of use to practitioners. In addition to these best practices, this study identifies additional areas in which future research should be concentrated.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.001
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 head0.158
GPT teacher head0.404
Teacher spread0.247 · 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