A model-based approach for testing the performance of web applications
Why this work is in the frame
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Bibliographic record
Abstract
Poor performance of Web-based systems can adversely impact the profitability of enterprises that rely on them. As a result, effective performance testing techniques are essential for understanding whether a Web-based system will meet its performance objectives when deployed in the real world. The workload of a Web-based system has to be characterized in terms of sessions; a session being a sequence of inter-dependent requests submitted by a single user. Dependencies arise because some requests depend on the responses of earlier requests in a session. To exercise application functions in a representative manner, these dependencies should be reflected in the synthetic workloads used to test Web-based systems. This makes performance testing a challenge for these systems. In this paper, we propose a model-based approach to address this problem. Our approach uses an application model that captures the dependencies for a Web-based system under study. Essentially, the application model can be used to obtain a large set of valid request sequences representing how users typically interact with the application. This set of sequences can be used to automatically construct a synthetic workload with desired characteristics. The application model provides an indirection which allows a common set of workload generation tools to be used for testing different applications. Consequently, less effort is needed for developing and maintaining the workload generation tools and more effort can be dedicated towards the performance testing process.
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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 | 0.000 |
| 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