MétaCan
Menu
Back to cohort
Record W2603990485 · doi:10.1109/ms.2017.31

What Do Developers Use the Crowd For? A Study Using Stack Overflow

2017· article· en· W2603990485 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

VenueIEEE Software · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceHelpfulnessConstruct (python library)Software engineeringCrowdsourcingStack (abstract data type)World Wide WebSoftwareSoftware developmentCall stackProgramming language

Abstract

fetched live from OpenAlex

Stack Overflow relies on the crowd to construct quality developer-related knowledge. To determine what developers use this knowledge for, researchers analyzed 1,414 Stack Overflow-related code commits. The developers used this knowledge to support development tasks and collect user feedback. The researchers also studied Stack Overflow posts' helpfulness and timeliness. The crowd was the most helpful on topics such as development tools and programming languages. The questions that took the longest to resolve were related to Web frameworks. The study findings can help developers better understand how to effectively use Stack Overflow, can help Stack Overflow designers improve their platform, and can help the research community understand Stack Overflow's strengths and weaknesses as a development tool. This article is part of a special issue on Crowdsourcing for Software Engineering.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0060.005
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 head0.097
GPT teacher head0.342
Teacher spread0.245 · 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