Studying the Change History of Code Snippets on Stack Overflow and GitHub
Bibliographic record
Abstract
Stack Overflow (SO) is a popular Q&A forum for software developers, providing a large number of copyable code snippets.While GitHub is an independent code collaboration platform, developers often reuse SO code in their GitHub projects.These code snippets get revised and edited on both platforms after their creation and adoption.In this work, we investigate such evolution of SO posts and their adapted code snippets on GitHub.To accomplish our goal, we mine the SOTorrent dataset that provides connectivity between code snippets on the SO posts with software projects hosted on GitHub.We then create an evolutionary history of adapted code snippets by mining 26K GitHub projects to study their evolution, by creating a new dataset GHCodeSnippetHistory.Along with studying characteristics of GitHub projects that reference SO posts and what famous SO discussions can be found in GitHub projects.Our results demonstrate that, on average, developers make 45 references to SO posts in their projects, with the highest number of references being made within the JavaScript code.We also found that 79% of the SO posts with code snippets that are referenced in GitHub code do change over time (at least ones), raising code maintainability and reliability concerns.The evolution of snippets on both platforms is driven by the original author of posts (SO) and adapted code snippets (GH).Finally, our results show the adapted snippets on GH and corresponding posts on SO evolve independently of one another.iii I, Saraj Singh Manes, would like to express my sincere gratitude to my amazing supervisor, Professor Olga Baysal, for her continuous guidance, advice, and friendly discussions throughout my thesis.Our joint vision for this project, her continuous efforts in providing me with valuable feedback and support made this work successful.I am very grateful to my family -my parents
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".