Analyzing and Forecasting Near-Miss Clones in Evolving Software: An Empirical Study
Bibliographic record
Abstract
Effort for development and maintenance of complex large software is believed to have dependency on the amount of duplicated code fragments (code clones) present in code-bases. For example, clones need to be carefully and consistently maintained and/or refactored for preventing accidental error propagation. Thus it is important to understand the proportion and evolution of clones in evolving software systems for cost estimation or the like. This paper presents a study on the evolution of near-miss clones at release level in medium to large open source software systems of different types (operating systems, database systems, editors, etc.) written in three different programming languages namely C, C#, and Java. Using a hybrid clone detector, NiCad, we detected both exact and near-miss clones at different levels of similarity. Applying statistical methods we investigated, from different dimensions, the evolution of both exact and near-miss clones, and also forecasted the amount of clones in future releases of the software systems. Our study offers significant insights into the existence and evolution of code clones and their relationships with programming language or paradigm and program size.
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.
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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".