On the comprehension of code clone visualizations: A controlled study using eye tracking
Bibliographic record
Abstract
Code clone visualizations (CCVs) are graphical representations of clone detection results provided by various state-of-the-art command line and graphical analysis tools. In order to properly analyze and manipulate code clones within a target system, these visualizations must be easily and efficiently comprehensible. We conducted an eye-tracking study with 20 participants (expert, intermediate, and novice) to assess how well people can comprehend visualizations such as Scatter plots, Treemaps, and Hierarchical Dependency Graphs provided by VisCad, a recent clone visualization tool. The goals of the study were to find out what elements of the visualizations (e.g., colors, shapes, object positions) are most important for comprehension, and to identify common usage patterns for different groups. Our results help us understand how developers with different levels of expertise explore and navigate through the visualizations while performing specific tasks. Distinctive patterns of eye movements for different visualizations were found depending on the expertise of the participants. Color, shape and position information were found to play vital roles in comprehension of CCVs. Our results provide recommendations that can improve the implementation of visualization techniques in VisCad and other clone visualization systems.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".