An Empirical Study of the Copy and Paste Behavior during Development
Bibliographic record
Abstract
Developers frequently employ Copy and Paste. However, little is known about the copy and paste behavior during development. To better understand the copy and paste behavior, automated approaches are proposed to identify cloned code. However, such automated approaches can only identify the location of the code that has been copied and pasted, but little is known about the context of the copy and paste. On the other hand, prior research studying actual copy and paste behavior is based on a small number of users in an experimental setup. In this paper, we study the behavior of developers copying and pasting code while using the Eclipse IDE. We mine the usage data of over 20,000 Eclipse users. We aim to explore the different patterns of Copy and Paste (C&P) that are used by Eclipse users during development. We compare such usage patterns to the regular users' usage of copy and paste during non-development tasks reported in earlier studies. Our findings instruct builders of future IDEs. We find that developers' C&P behavior is considerably different from the behavior of regular users. For example, developers tend to perform more frequent C&P in the same file contrary to regular users, who tend to perform C&P across different windows. Moreover, we find that C&P across different programming languages is a common behavior as we extracted more than 75,000 C&P incidents across different programming languages. Such a finding highlights the need for code cloning tools that can detect code clones across different programming languages.
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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 | 0.006 | 0.074 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".