Understanding How Developers Reuse Stack Overflow Code in Their GitHub Projects
Bibliographic record
Abstract
Stack Overflow and other popular Q&A forums include a variety of reusable code snippets for software developers.Instead of writing new code, most software developers prefer to reuse existing code which in a software projects, this reuse of code is referred to as "code cloning".In this study we look into how software developers reused and adopted code snippets from Stack Overflow in projects hosted on GitHub.To achieve our goal, we create a code pair dataset that maps Stack Overflow code snippets to GitHub commits with the help of SOTorrent and GHCodeSnippetHistory.Our dataset consists of code pairs from four programming languages including Java, JavaScript, PHP, and Python.The first part of the study concentrates on finding clones between Stack Overflow and GitHub code snippets and its challenges.The result of first part indicates around Two years after starting my Master's program, now I'm pleased to write the last note in my thesis.This research work expanded my expertise on a very interesting aspect of computer science dealing with software engineering, mining software repositories, and data science.I would like to mention the people who have continuously supported and directed me all the way through my graduate school journey.My special thanks go to my research supervisor, Dr. Olga Baysal, for providing me with this great opportunity to work under her supervision.I am sincerely thankful for all of her
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".