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Record W4415439552 · doi:10.63345/sjaibt.v2.i3.302

Browser-Based IDE for C/C++ Compilation

2025· article· W4415439552 on OpenAlexaff
Richard Collins

Bibliographic record

VenueScientific Journal of Artificial Intelligence and Blockchain Technologies · 2025
Typearticle
Language
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsSoftwareCloud computingKey (lock)CompilerSoftware developmentSoftware system

Abstract

fetched live from OpenAlex

The rapid growth of cloud computing, containerization, and WebAssembly technologies has redefined the paradigm of software development, paving the way for browser-based Integrated Development Environments (IDEs) capable of compiling and executing complex languages such as C and C++. This study presents a comprehensive exploration of a browser-based IDE designed specifically for C/C++ development, examining its architecture, implementation strategies, performance benchmarks, and applicability across educational and professional contexts. Unlike conventional desktop IDEs, the proposed system removes installation and configuration overhead, enabling instant access from any internet-enabled device while maintaining secure, isolated, and high-performance compilation environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.306
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

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

Citations0
Published2025
Admission routes1
Has abstractyes

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