An Empirical Comparison on the Results of Different Clone Detection Setups for C-based Projects
Bibliographic record
Abstract
Code clones have been used in many different software maintenance and evaluation tasks in practice (e.g., change proportion and evolution, refactoring, and vulnerability management). There are many clone detection techniques (e.g., text-based, token-based, and AST-based) which can detect code clones not only at the source code-level but also at the compiled artifacts (e.g., IR or binary) level. Unfortunately, there are few studies which thoroughly compare the results of various clone detection setups (a.k.a., different clone detection techniques applied at different artifacts), especially for C-based projects. Therefore, in this paper, we conduct a systematic study to compare the effectiveness of six different code clone detection setups. Each setup, which uses the representative one of the three clone detection techniques: token-based technique (SourcererCC), AST-based (NiCad) technique, and text-based (MSFinder) technique, is applied either at the source code-level or at the LLVM-based IR-level. We conduct our experiments on five C-based open-source systems, Apache, Python, PostgreSQL, FFmpeg, and Linux kernel. Experimental results show that the AST-based technique is better than the token-based and text-based techniques, and clone detection setups performed at the IR-level generally yield higher performance than those performed at the source code-level. The setup of AST-based technique applied at the IR-level, yields the highest performance overall, with an F-score of 84%. However, there is no one setup which can detect all the clones. Through manual qualitative analysis, we have identified ten reasons why certain clones cannot be detected at the IR level or at the source code-level, and two reasons why one of the techniques fails to detect clones. Our findings highlight the usefulness of conducting clone detection under different setups. Furthermore, this study also motivates the need for more application-oriented clone comparison 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.
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 | 0.016 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".