Using indexed sequence diagrams to recover the behaviour of AJAX applications
Bibliographic record
Abstract
AJAX is an asynchronous client-side technology that enables feature-rich, interactive Web 2.0 applications. AJAX applications and technologies are very complex compared to classic web applications, having to cope with asynchronous communication over (unstable) network connections. Yet, AJAX developers still rely on the ad hoc development processes and techniques of the early '00s. To determine how the inherent complexity of AJAX impacts the design and maintenance of AJAX applications, this paper studies the amount of code reuse across the different features of an AJAX application. Furthermore, we analyze how the design of existing AJAX systems deal with AJAX-specific crosscutting concerns, such as handling the loss of network connectivity. We use dynamic analysis to recover the run-time behaviour of AJAX applications in the form of sequence diagrams that are indexed by the different asynchronous communication states that the application can be in. Exploratory case studies on three AJAX applications show that (1) a majority (60-90%) of the run-time behaviour is shared, theoretically simplifying maintenance, and (2) that the studied projects seem unprepared for loss of network connectivity, often presenting the user with an incorrect view of the application state.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 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".