Corresponding formal specifications with distributed systems
Bibliographic record
Abstract
As the need for computing resources grows, providers are increasingly relying on distributed systems to render their services. However, distributed systems are hard to design and implement. As an aid for design and implementation, formal verifica- tion has seen a growing interest in industry. For example, Amazon uses Temporal Logic of Actions plus (TLA⁺) and PlusCal specification languages and tool chain to formally verify manually created specifications of their web services [8]. Nevertheless, there is currently no tool to automatically establish a correspon- dence between a PlusCal specification with a concrete implementation. Further- more, PlusCal was not designed with modularity in mind, so a large PlusCal spec- ification cannot be decomposed into smaller ones for ease of modification. This thesis proposes an extension to PlusCal, named Modular PlusCal, as well as a compiler, named PGo, which compiles Modular PlusCal and PlusCal specifications into Go programs. Modular PlusCal introduces new constructs, such as archetypes and mapping macros, to provide isolation and, as a result, modularity. By auto- matically compiling PlusCal and Modular PlusCal specifications into distributed system implementations, PGo reduces the burden on programmers trying to ensure the correctness of their distributed systems.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".