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Record W2013410000 · doi:10.1145/1353482.1353501

View-based maintenance of graphical user interfaces

2008· article· en· W2013410000 on OpenAlexaff
Peng Li, Eric Wohlstadter

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceGraphical user interfaceSource codeSoftware maintenanceContext (archaeology)Graphical user interface testingProgramming languageHuman–computer interactionUser interfaceSoftware engineeringObject (grammar)Code (set theory)Plug-inObject-oriented programmingSoftwareInterface (matter)Software systemUser interface designOperating systemSet (abstract data type)Artificial intelligence

Abstract

fetched live from OpenAlex

One difficulty in software maintenance is that the relationship between observed program behavior and source code is not always clear. In this paper we are concerned specifically with the maintenance of graphical user interfaces (GUIs). User interface code can crosscut the decomposition of applications making GUIs hard to maintain. A popular approach to develop and maintain GUIs is to use "What you see is what you get" editors. They allow developers to work directly with a graphical design view instead of scattered source elements. Unfortunately GUI editors are limited by their ability to statically reconstruct dynamic collaborations between objects. In this paper we investigate the combination of a hybrid dynamic and static approach to allow for view-based maintenance of GUIs. Dynamic analysis reconstructs object relationships, providing a concrete context in which maintenance can be performed. Static checking restricts that only changes in the design view which can meaningfully be translated back to source are allowed. We implemented a prototype IDE plug-in and evaluate our approach by applying it to five open source projects.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.214

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 communication0.0000.000
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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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 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

Citations20
Published2008
Admission routes1
Has abstractyes

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