FLAG: <u>F</u> inding <u>L</u> ine <u>A</u> nomalies (in RTL code) with <u>G</u> enerative AI
Bibliographic record
Abstract
Bug detection in Hardware Design Languages (HDLs) is an important problem in the System-on-Chip (SoC) development cycle. It is crucial to find defects at the earliest stage possible. While most fault localization requires the use of “tests” (e.g., test benches, fuzzing, and assertions) and a simulation or emulation framework, the advent of Large Language Models (LLMs) provides an opportunity for a test-free fault localization approach. This article proposes such a tool, called FLAG, which can identify functional and security defects in Register Transfer Level (RTL) code without synthesis or simulation. FLAG combines syntactic and generative AI techniques to implement fault localization in RTL code. It takes an RTL design as an input and outputs a set of line(s) that likely contain defects. It targets elements of RTL code most likely to contain bugs through static analysis means and then implements token-level and line-level analysis to obtain differences in original code and code generated by LLM to identify a line as buggy or not. The token-level approach evaluates each generated token (one at a time) and the line level approach evaluates the entire line generated by the LLM. We evaluate our approach on a corpus of synthetic and real-world bugs, of both functional and security related issues, in Verilog and SystemVerilog. Using line-level analysis, FLAG can identify 38 out of 120 real-world bugs and using token-level analysis, FLAG can identify 32 out of 81 synthetic bugs through the top-5 most likely bug locations identified without tests.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".