A Fine-Grained Analysis on the Inconsistent Changes in Code Clones
Bibliographic record
Abstract
Existing studies report that inconsistent changes in code clones can introduce bugs or inconsistencies in a software system's code-base. However, inconsistent changes can often be intentional and these might not lead to bugs. Thus, it would be beneficial if we could have an automatic mechanism for proactively determining which inconsistent changes are likely to introduce bugs in a code-base. With this focus, in our research we investigate the underlying factors affecting the possibility that an inconsistent change made to code clones will lead to bugs. We extract the evolutionary history of the clone fragments in open-source software systems and analyze whether clone-types, former clone evolutionary patterns, and clone proximity have impacts on the bug-proneness of the inconsistent changes to clones. We perform our investigation on six subject systems written in three different programming languages (Java, C, and C#) and find that inconsistent changes in Type 3 clones have the highest possibility of introducing bugs among all three clone-types (Type 1, 2, and 3). Moreover, similarity preserving inconsistent changes are significantly more likely to introduce bugs compared to diverging inconsistent changes. Proximity as well as granularity of code clones have significant impacts on their possibilities of experiencing bug-fixes after having inconsistent changes. Findings from our research can be important for minimizing bugs and inconsistencies in software systems during their evolution.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".