Bibliographic record
Abstract
To programmatically interact with the user interface of a web application, element locators are used to select and retrieve elements from the Document Object Model (DOM). Element locators are used in JavaScript code, Cascading stylesheets, and test cases to interact with the runtime DOM of the webpage. Constructing these element locators is, however, challenging due to the dynamic nature of the DOM. We find that locators written by web developers can be quite complex, and involve selecting multiple DOM elements. We present an automated technique for synthesizing DOM element locators using examples provided interactively by the developer. The main insight in our approach is that the problem of synthesizing complex multi-element locators can be expressed as a constraint solving problem over the domain of valid DOM states in a web application. We implemented our synthesis technique in a tool called LED, which provides an interactive drag and drop support inside the browser for selecting positive and negative examples. We find that LED supports at least 86% of the locators used in the JavaScript code of deployed web applications, and that the locators synthesized by LED have a recall of 98% and a precision of 63%. LED is fast, taking only 0.23 seconds on average to synthesize a locator.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".