MétaCan
Menu
Back to cohort
Record W2022090474 · doi:10.1109/msr.2013.6624012

Making sense of online code snippets

2013· article· en· W2022090474 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
TopicSoftware Engineering Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceSnippetAndroid (operating system)Information retrievalSource codeCode (set theory)World Wide WebCallbackProgramming languageSet (abstract data type)Operating system

Abstract

fetched live from OpenAlex

Stack Overflow contains a large number of high-quality source code snippets. The quality of these snippets has been verified by users marking them as solving a specific problem. Stack Overflow treats source code snippets as plain text and searches surface snippets as they would any other text. Unfortunately, plain text does not capture the structural qualities of these snippets; for example, snippets frequently refer to specific API (e.g., Android), but by treating the snippets as text, linkage to the Android API is not always apparent. We perform snippet analysis to extract structural information from short plain-text snippets that are often found in Stack Overflow. This analysis is able to identify 253,137 method calls and type references from 21,250 Stack Overflow code snippets. We show how identifying these structural relationships from snippets could perform better than lexical search over code blocks in practice.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.607
Threshold uncertainty score0.176

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)0.0000.000

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 head0.045
GPT teacher head0.316
Teacher spread0.271 · 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

Citations66
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicSoftware Engineering ResearchFrench-language works237,207