Cloning by accident: an empirical study of source code cloning across software systems
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.
Bibliographic record
Abstract
One of the key goals of open source development is the sharing of knowledge, experience, and solutions that pertain to a software system and its problem domain. Source code cloning is one way in which expertise can be reused across systems; cloning is known to have been used in several open source projects, such as the SCSI drivers of the Linux kernel. In this paper, we discuss two case studies in which we performed clone detection on several open source systems within the same domain. In the first case study we examined nine text editors written in C, and in the second study we examined eight X-Windows window managers written in C and C++. To our surprise, we found little evidence of "true" cloning activity, but we did notice a significant number of "accidental" clones - that is, code fragments that are similar due to the precise protocols they must use when interacting with a given API or set of libraries. We further discuss the nature of "true" versus "accidental" clones, as well as the details of our case studies.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it