TraceLinking Implementations with Their Verified Designs
Bibliographic record
Abstract
An important correctness gap exists between formally verifiable distributed system designs and their implementations. Recently proposed work bridges this gap by automatically extracting, or compiling, an implementation from the formally-verified design. The runtime behavior of this compiled implementation, however, may deviate from its design. For example, the compiler may contain bugs, the design may make incorrect assumptions about the deployment environment, or the implementation might be misconfigured. In this paper we develop TraceLink, a methodology to detect such deviations through trace validation. TraceLink maps traces, that capture an execution’s behavior, to the corresponding formal design. Unlike previous work on trace validation, our approach is completely automated. We implement TraceLink for PGo, a compiler from Modular PlusCal to both TLA + and Go. We present a formal semantics for interpreting execution traces as TLA + , along with a templatization strategy to minimize the size of the TLA + tracing specification. We also present a novel trace path validation strategy, called sidestep , which detects bugs faster and with little additional overhead. We evaluated TraceLink on several distributed systems, including an MPCal implementation of a Raft key-value store. Our evaluation demonstrates that TraceLink is able to find 9 previously undetected and diverse bugs in PGo’s TCB, including a bug in the PGo compiler itself. We also show the effectiveness of the templatization approach and the sidestep path validation strategy.
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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 | 0.011 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".