Understanding JavaScript Event-Based Interactions with Clematis
Bibliographic record
Abstract
Web applications have become one of the fastest-growing types of software systems today. Despite their popularity, understanding the behavior of modern web applications is still a challenging endeavor for developers during development and maintenance tasks. The challenges mainly stem from the dynamic, event-driven, and asynchronous nature of the JavaScript language. We propose a generic technique for capturing low-level event-based interactions in a web application and mapping those to a higher-level behavioral model. This model is then transformed into an interactive visualization, representing episodes of triggered causal and temporal events, related JavaScript code executions, and their impact on the dynamic DOM state. Our approach, implemented in a tool called C lematis , allows developers to easily understand the complex dynamic behavior of their application at three different semantic levels of granularity. Furthermore, C lematis helps developers bridge the gap between test cases and program code by localizing the fault related to a test assertion. The results of our industrial controlled experiment show that C lematis is capable of improving the comprehension task accuracy by 157% while reducing the task completion time by 47%. A follow-up experiment reveals that C lematis improves the fault localization accuracy of developers by a factor of two.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".