IPA: Error Propagation Analysis of Multi-Threaded Programs Using Likely Invariants
Bibliographic record
Abstract
Error Propagation Analysis (EPA) is a technique forunderstanding how errors affect a program's execution and resultin program failures. For this purpose, EPA usually compares thetraces of a fault-free (golden) run with those from a faulty run ofthe program. This makes existing EPA approaches brittle for multithreadedprograms, which do not typically have a deterministicgolden run. In this paper, we study the use of likely invariantsgenerated by automated approaches as alternatives for goldenrun based EPA in multithreaded programs. We present InvariantPropagation Analysis (IPA), an approach and a framework forautomatically deriving invariants for multithreaded programs, and using the invariants for EPA. We evaluate the invariantsderived by IPA in terms of their coverage for different faulttypes across six representative programs through fault injectionexperiments. We find that stable invariants can be inferred in allsix programs, although their coverage of faults depends on theapplication and the fault type.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".