Supporting the analysis of clones in 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
Abstract Code duplication is a well‐documented problem in industrial software systems. There has been considerable research into techniques for detecting duplication in software, and there are several effective tools to perform this task. However, there have been few detailed qualitative studies into how cloning actually manifests itself within software systems. This is primarily due to the large result sets that many clone‐detection tools return; these result sets are very difficult to manage without complementary tool support that can scale to the size of the problem, and this kind of support does not currently exist. In this paper we present an in‐depth case study of cloning in a large software system that is in wide use, the Apache Web server; we provide insights into cloning as it exists in this system, and we demonstrate techniques to manage and make effective use of the large result sets of clone‐detection tools. In our case study, we found several interesting types of cloning occurrences, such as ‘cloning hotspots’, where a single subsystem comprising only 17% of the system code contained 38.8% of the clones. We also found several examples of cloning behavior that were beneficial to the development of the system, in particular cloning as a way to add experimental functionality. Copyright © 2006 John Wiley & Sons, Ltd.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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