Bibliographic record
Abstract
Formal verification has, in recent years, become widely used in the design and implementation of large integrated circuits, but its use in general software verification has been more limited. We have developed a new technique to verify assembly code for digital signal processors (DSPs) that makes significant steps into the realm of software verification and serves as a good building block for future verification efforts. In order to demonstrate the applicability of our approach, which takes inspiration from successful techniques applied in hardware verification, we have implemented a prototype tool to verify assembly code for a Fujitsu DSP chip. The approach we have created is based on symbolic simulation with uninterpreted functions and control flow analysis. DSP assembly language programs are an attractive target for formal verification. On one hand, DSP assembly language programs must often be modified for size and speed constraints which requires that the code be optimized by taking advantage of the idiosyncrasies of the chip. This optimization can make even small programs hard to reason about and debug. On the other hand, verification of optimized versus unoptimized versions of the same program can be simplified by exploiting the similarities between the two. This combination produces an application domain that is simultaneously challenging yet tractable. This thesis describes our verification approach and how we were able to successfully implement a prototype tool.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".