Supporting the analysis of clones in software systems
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.
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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 | 0.007 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".