Bibliographic record
Abstract
Since there are a tremendous number of similar functionalities related to images, 3D graphics, sounds, and script in games software, there is a common wisdom that there might be more cloned code in games compared to traditional software. Also, there might be more cloned code across games since many of these games share similar strategies and libraries. In this study, we attempt to investigate whether such statements are true by conducting a large empirical study using 32 games and 9 non-games software, written in three different programming languages C, Java, and C#, for the case of both exact and near-miss clones. Using a hybrid clone detection tool NiCad and a visualization tool VisCad, we examine and compare the cloning status in them and compare it to the non-games, and examine the cloned methods across game engines. The results show that code reuse in open source games is much different from that of other software systems. Specifically, in contrast to the common wisdom, there are fewer function clones in game open source comparing to non-game open source software systems. Similar to non-games open source, we observed that cloning status changes between different programming languages of the games. In addition, there are very fewer clones across games and mostly no clones (no code reuse) across different game engines. But clones exist heavily across recreated (cloned) games.
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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 | 0.012 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".