Anomaly detection in performance regression testing by transaction profile estimation
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Bibliographic record
Abstract
Summary As part of the process to test a new release of an application, the performance testing team need to confirm that the existing functionalities do not perform worse than those in the previous release, a problem known as performance regression anomaly. Most existing approaches to analyse performance regression testing data vary according to the applied workload, which usually leads to the need for an extra performance testing run. To ease such lengthy tasks, we propose a new workload‐independent, automated technique to detect anomalies in performance regression testing data using the concept known as transaction profile (TP). The TP is inferred from the performance regression testing data along with the queueing network model of the testing system. Based on a case study conducted against two web applications, one open source and one industrial, we have been able to automatically generate the ‘TP run report’ and verify that it can be used to uncover performance regression anomalies caused by software updates. In particular, the report helped us to isolate the real anomaly issues from those caused by workload changes with an average F1 measure of 85% for the open source application and 90% for the industrial application. Such results support our proposal to use the TP as a more efficient technique in identifying performance regression anomalies than the state of the art industry and research techniques. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 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