Error Invariants for Concurrent Traces
Bibliographic record
Abstract
Error invariants are assertions that over-approximate the reachable program states at a given position in an error trace while only capturing states that will still lead to failure if execution of the trace is continued from that position. Such assertions reflect the effect of statements that are involved in the root cause of an error and its propagation, enabling slicing of statements that do not contribute to the error. Previous work on error invariants focused on sequential programs. We generalize error invariants to concurrent traces by augmenting them with additional information about hazards such as write-after-write events, which are often involved in race conditions and atomicity violations. By providing the option to include varying levels of details in error invariants-such as hazards and branching information-our approach allows the programmer to systematically analyze individual aspects of an error trace.We have implemented a hazard-sensitive slicing tool for concurrent traces based on error invariants and evaluated it on benchmarks covering a broad range of real-world concurrency bugs. Hazard-sensitive slicing significantly reduced the length of the considered traces and still maintained the root causes of the concurrency bugs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".