Directed test generation and analysis for web applications
Bibliographic record
Abstract
The advent of web technologies has led to the proliferation of modern web applications with enhanced user interaction and client-side execution. JavaScript (the most widely used programming language) is extensively used to build responsive modern web applications. The event-driven and dynamic nature of JavaScript, and its interaction with the Document Object Model (DOM), make it challenging to understand and test effectively. The ultimate goal of this thesis is to improve the quality of web applications through automated testing and maintenance. The work presented in this dissertation has focused on advancing the state-of-the-art in testing and maintaining web applications by proposing a new set of techniques and tools. We proposed (1) a feedback-directed exploration technique and a tool to cover a subset of the state-space of a given web application; the exploration is guided towards achieving higher functionality, navigational, and page structural coverage while reducing the test model size, (2) a technique and a tool to generate UI tests using existing tests; it mines the existing test suite to infer a model of the covered DOM states and event-based transitions including input values and assertions; it then expands the inferred model by exploring alternative paths and generates assertions for the new states; finally it generates a new test suite from the extended model, (3) the first empirical study on JavaScript tests to characterize their prevalence and quality metrics, and to find out root causes for the uncovered (missed) parts of the code under test, (4) a DOM-based JavaScript test fixture generation technique and a tool, which is based on dynamic symbolic execution; it guides the executing through different branches of a function by producing expected DOM instances, (5) a technique and a tool to detect JavaScript code smells using static and dynamic analysis. We evaluated the presented techniques by conducting various empirical studies and comparisons. The evaluation results point to the effectiveness of the proposed techniques in terms of fault detection capability and code coverage for test generation, and in terms of accuracy for code smell detection.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".