Efficient Replay-based Regression Testing for Distributed Reactive Systems in the Context of Model-driven Development
Why this work is in the frame
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Bibliographic record
Abstract
As software evolves, regression testing techniques are typically used to ensure the new changes are not adversely affecting the existing features. Despite recent advances, regression testing for distributed systems remains challenging and extremely costly. Existing techniques often require running a failing system several time before detecting a regression. As a result, conventional approaches that use re-execution without considering the inherent non-determinism of distributed systems, and providing no (or low) control over execution are inadequate in many ways. In this paper, we present MRegTest, a replay-based regression testing framework in the context of model-driven development to facilitate deterministic replay of traces for detecting regressions while offering sufficient control for the purpose of testing over the execution of the changed system. The experimental results show that compared to the traditional approaches that annotate traces with timestamps and variable values MRegTest detects almost all regressions while reducing the size of the trace significantly and incurring similar runtime overhead.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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