Explainable Recommendation of Software Vulnerability Repair Based on Metadata Retrieval and Multifaceted LLMs
Bibliographic record
Abstract
Common Weakness Enumerations (CWEs) and Common Vulnerabilities and Exposures (CVEs) are open knowledge bases that provide definitions, descriptions, and samples of code vulnerabilities. The combination of Large Language Models (LLMs) with vulnerability knowledge bases helps to enhance and automate code vulnerability repair. Several key factors come into play in this setting, including (1) the retrieval of the most relevant context to a specific vulnerable code snippet; (2) augmenting LLM prompts with the retrieved context; and (3) the generated artifact form, such as a code repair with natural language explanations or a code repair only. Artifacts produced by these factors often lack transparency and explainability regarding the rationale behind the repair. In this paper, we propose an LLM-enabled framework for explainable recommendation of vulnerable code repairs with techniques addressing each factor. Our method is data-driven, which means the data characteristics of the selected CWE and CVE datasets and the knowledge base determine the best retrieval strategies. Across 100 experiments, we observe the inadequacy of the SOTA metrics to differentiate between low-quality and irrelevant repairs. To address this limitation, we design the LLM-as-a-Judge framework to enhance the robustness of recommendation assessments. Compared to baselines from prior works, as well as using static code analysis and LLMs in zero-shot, our findings highlight that multifaceted LLMs guided by retrieval context produce explainable and reliable recommendations under a small to mild level of self-alignment bias. Our work is developed on open-source knowledge bases and models, which makes it reproducible and extensible to new datasets and retrieval strategies.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".