Automated Acceptance Testing of JavaScript Web Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Acceptance testing is an important part of software development and it is performed to ensure that a system delivers its required functionalities. Today, most modern interactive web applications are designed using Web 2.0 technologies, many among them relying on JavaScript. JavaScript enables the development of client-side functionality through the dynamic modification of the web-page's content and structure without calls to the server. This implies that server-side testing frameworks will necessarily fail to test the complete application behaviors. In this paper we present a method for automated acceptance testing of JavaScript web applications to ensure that required functionalities have been implemented. Using an intuitive, human-readable scripting language our method allows users to describe user stories in high level declarative test scripts and to then execute these test scripts on a web application using an automated website crawler. We also describe a case study that evaluates our approach in terms of capabilities to translate user stories in automated acceptance test scripts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it