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Record W4244130460 · doi:10.1109/icse.2000.870504

Exploring O-O framework usage

2002· article· en· W4244130460 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

VenueProceedings of the 2000 International Conference on Software Engineering. ICSE 2000 the New Millennium · 2002
Typearticle
Languageen
FieldComputer Science
TopicWeb Applications and Data Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceConstruct (python library)Software engineeringDomain (mathematical analysis)Object-oriented programmingSoftwareProgramming language

Abstract

fetched live from OpenAlex

Object-oriented application frameworks are becoming an increasingly popular part of software development but there has been little work on studying how they are actually used. An object-oriented application framework comprises a design and an object-oriented implementation of that design meant to apply to a broad range of applications, or subsystems within a single domain, such as graphical user interfaces. FrameScan is an ongoing study with the goal of understanding how developers can effectively and efficiently understand and deploy framework technology to construct and evolve their applications. In order to study how frameworks are used, 34 students of a senior year software engineering course were divided up into six teams of five or six students each. Each team had three months to design and implement a small client-server application of their choosing with the requirement that a framework for client-server computing called CSF (Client-Server Framework) be used as part of the project. The conclusions of the study are presented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.666

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 science0.0030.001
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 head0.066
GPT teacher head0.236
Teacher spread0.169 · 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