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
Code cloning is a controversial software engineering practice due to contradictory claims regarding its effect on software maintenance. Code stability is a recently introduced measurement technique that has been used to determine the impact of code cloning by quantifying the changeability of a code region. Although most existing stability analysis studies agree that cloned code is more stable than non-cloned code, the studies have two major flaws: (i) each study only considered a single stability measurement (e.g., lines of code changed, frequency of change, age of change); and, (ii) only a small number of subject systems were analyzed and these were of limited variety. In this paper, we present a comprehensive empirical study on code stability using four different stability measuring methods. We use a recently introduced hybrid clone detection tool, NiCAD, to detect the clones and analyze their stability in different dimensions: by clone type, by measuring method, by programming language, and by system size and age. Our in-depth investigation on 12 diverse subject systems written in three programming languages considering three types of clones reveals that: (i) cloned code is generally less stable than non-cloned code, and more specifically both Type-1 and Type-2 clones show higher instability than Type-3 clones; (ii) clones in both Java and C systems exhibit higher instability compared to the clones in C# systems; (iii) a system's development strategy might play a key role in defining its comparative code stability scenario; and, (iv) cloned and non-cloned regions of a subject system do not follow any consistent change pattern.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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