Investigating Context Adaptation Bugs in Code Clones
Bibliographic record
Abstract
The identical or nearly similar code fragments in a code-base are called code clones. There is a common belief that code cloning (copy/pasting code fragments) can introduce bugs in a software system if the copied code fragments are not properly adapted to their contexts (i.e., surrounding code). However, none of the existing studies have investigated whether such bugs are really present in code clones. We denote these bugs as Context Adaptation Bugs, or simply Context-Bugs, in our paper and investigate the extent to which they can be present in code clones. We define and automatically analyze two clone evolutionary patterns that indicate fixing of Context-Bugs. According to our analysis on thousands of revisions of six open-source subject systems written in Java, C, and C#, code cloning often introduces Context-Bugs in software systems. Around 50% of the clone related bug-fixes can occur for fixing Context-Bugs. Cloning (copy/pasting) a newly created code fragment (i.e., a code fragment that was not added in a former revision) is more likely to introduce Context-Bugs compared to cloning a preexisting fragment (i.e., a code fragment that was added in a former revision). Moreover, cloning across different files appears to have a significantly higher tendency of introducing Context-Bugs compared to cloning within the same file. Finally, Type 3 clones (gapped clones) have the highest tendency of containing Context-Bugs among the three major clone-types. Our findings can be important for early detection as well as removal of Context-Bugs in code clones.
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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 | 0.002 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".