Automated analysis of CSS rules to support style maintenance
Bibliographic record
Abstract
CSS is a widely used language for describing the presentation semantics of HTML elements on the web. The language has a number of characteristics, such as inheritance and cascading order, which makes maintaining CSS code a challenging task for web developers. As a result, it is common for unused rules to be accumulated over time. Despite these challenges, CSS analysis has not received much attention from the research community. We propose an automated technique to support styling code maintenance, which (1) analyzes the runtime relationship between the CSS rules and DOM elements of a given web application (2) detects unmatched and ineffective selectors, overridden declaration properties, and undefined class values. Our technique, implemented in an open source tool called Cilla, has a high precision and recall rate. The results of our case study, conducted on fifteen open source and industrial web-based systems, show an average of 60% unused CSS selectors in deployed applications, which points to the ubiquity of the problem.
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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 | 0.000 |
| 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".