MRegTest: A Replay-Based Regression Testing Tool for Distributed UML-RT Models
Bibliographic record
Abstract
Regression testing is indispensable, especially for real-time distributed systems to ensure that existing functionalities are not affected by changes. In this paper, we present MRegTest, a replay-based regression testing tool for distributed systems that are developed using communicating state machine models. Despite recent advances, regression testing for distributed systems remains challenging. The inherent non-determinism typically allows systems to exhibit many different executions in response to the same input. In addition, it is often not possible to control the execution environment such that this non-determinism is removed without changing the execution semantics. MRegTest addresses the above-mentioned challenges via Automatic Mutant Generation (AMG) and Regression Testing (RT) modules. AMG facilitates regression testing by generating several mutants from a UML-RT model according to a user-defined set of critical variables. RT allows the user to detect regressions of both single or multiple modified models. It then reports regressions and enables the user to replay traces visually in a web-based application. We have evaluated MRegTest against several use cases with various complexities. 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. The tool demonstration video: https://youtu.be/lPXjmKgadQI
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".