An Industrial Case Study on the Automated Detection of Performance Regressions in Heterogeneous Environments
Bibliographic record
Abstract
Abstract—A key goal of performance testing is the detection of performance degradations (i.e., regressions) compared to previous releases. Prior research has proposed the automation of such analysis through the mining of historical performance data (e.g., CPU and memory usage) from prior test runs. Nevertheless, such research has had limited adoption in practice. Working with a large industrial performance testing lab, we noted that a major hurdle in the adoption of prior work (including our own work) is the incorrect assumption that prior tests are always executed in the same environment (i.e., labs). All too often, tests are performed in heterogenous environments with each test being run in a possibly different lab with different hardware and software configurations. To make automated performance regression analysis techniques work in industry, we propose to model the global expected behaviour of a system as an ensemble (combination) of individual models, one for each successful previous test run (and hence configuration). The ensemble of models of prior test runs are used to flag performance deviations (e.g., CPU counters showing higher usage) in new tests. The deviations are then aggregated using simple voting or more advanced weighting to determine whether the counters really deviate from the expected behaviour or whether it was simply due to an environment-specific variation. Case studies on two open-source systems and a very large scale industrial application show that our weighting approach outperforms a state-of-the-art environment-agnostic approach. Feedback from practitioners who used our approach over a 4 year period (across several major versions) has been very positive. I.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".