Does cloned code increase maintenance effort?
Bibliographic record
Abstract
In-spite of a number of in-depth investigations regarding the impact of clones in the maintenance phase there is no concrete answer to the long lived research question, “Does the presence of code clones increase maintenance effort?”. Existing studies have measured different change related metrics for cloned and non-cloned regions, however, no study calculates the maintenance effort spent for these code regions. In this paper, we perform an in-depth empirical study in order to compare the maintenance efforts required for cloned and non-cloned code. For the purpose of our study we implement a prototype tool which is capable of estimating the effort spent by a developer for changing a particular method. It can also predict effort that might need to be spent for making some changes to a particular method. Our estimation and prediction involve automatic extraction and analysis of the entire evolution history of a candidate software system. We applied our tool on hundreds of revisions of six open source subject systems written in three different programming languages for calculating the efforts spent for cloned and non-cloned code. According to our experimental results: (i) cloned code requires more effort in the maintenance phase than non-cloned code, and (ii) Type 2 and Type 3 clones require more effort compared to the efforts required by Type 1 clones. According to our findings, we should prioritize Type 2 and Type 3 clones when making clone management decisions.
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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 | 0.004 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".