A Reordering Framework for Testing Message-Passing Systems
Bibliographic record
Abstract
In a message-passing system (MPS), components communicate through messages. However, both the time and order in which messages are delivered depend on the execution environment. The resulting nondeterminism may lead to concurrency defects such as message races, making it difficult to thoroughly test and debug MPS. This paper presents a new framework for testing components of an MPS for faults that involve the violation of any implicit user-intended receiving order of messages. The framework purposefully assesses possible interleavings by reordering incoming messages before delivering them to the tested target component. We evaluate three methods to support the reordering process: the blocking method intercepts and blocks each message until all its dependencies have occurred, the buffering method buffers a message until either its dependencies are observed or a predefined timeout expires, and the adaptive buffering dynamically adjusts its flushing period. All three methods are implemented inside QNX Neutrino, a popular embedded real-time operating system. The evaluation shows a 4x speedup over random testing with minimal run time overhead and only a few kilobytes of memory overhead. These results confirms the effectiveness and capability of the framework to uncover faults in real-world applications.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".