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Record W4364321651 · doi:10.1109/tse.2023.3265855

Identifying Concepts in Software Projects

2023· article· en· W4364321651 on OpenAlex
Mathieu Nassif, Martin P. Robillard

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

VenueIEEE Transactions on Software Engineering · 2023
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceDocumentationSoftware engineeringDomain (mathematical analysis)Software project managementSoftware developmentSoftwareDomain analysisData scienceSoftware constructionProgramming language

Abstract

fetched live from OpenAlex

When working on a project, software developers must be familiar with computing concepts, standards, and technologies related to the project. We present a novel approach, called Scode, to automatically identify those concepts using the project's documentation. Scode combines entity linking and network analysis techniques specialized for the software development domain. In addition to concepts explicitly mentioned in the documentation, Scode can retrieve implicit concepts related to the project's domain. Concepts identified by Scode have a recognized meaning that is consistent across projects. We compared Scode to different baselines and found that it is more effective at mapping projects to a consistent concept space.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.824
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
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.296
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