MétaCan
Menu
Back to cohort
Record W1985206804 · doi:10.1109/esem.2013.9

Real Challenges in Mobile App Development

2013· article· en· W1985206804 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
TopicMobile and Web Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPopularityMobile appsComputer scienceContext (archaeology)Mobile deviceMobile computingMobile technologyMobile business developmentMobile WebInternet privacyWorld Wide WebData sciencePsychologyTelecommunications

Abstract

fetched live from OpenAlex

Context: Mobile app development is a relatively new phenomenon that is increasing rapidly due to the ubiquity and popularity of smartphones among end-users. Objective: The goal of our study is to gain an understanding of the main challenges developers face in practice when they build apps for different mobile devices. Method: We conducted a qualitative study, following a Grounded Theory approach, in which we interviewed 12 senior mobile developers from 9 different companies, followed by a semi-structured survey, with 188 respondents from the mobile development community. Results: The outcome is an overview of the current challenges faced by mobile developers in practice, such as developing apps across multiple platforms, lack of robust monitoring, analysis, and testing tools, and emulators that are slow or miss many features of mobile devices. Conclusion: Based on our findings of the current practices and challenges, we highlight areas that require more attention from the research and development community.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score1.000

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.029
GPT teacher head0.250
Teacher spread0.221 · 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

Citations378
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicMobile and Web ApplicationsFrench-language works237,207