Finding an Optimal Set of Static Analyzers To Detect Software Vulnerabilities
Bibliographic record
Abstract
Software vulnerabilities are ubiquitous and costly. To detect vulnerabilities earlier during development, organizations deploy a set of static analyzers to locate and eventually fix these vulnerabilities before releasing their software. Due to the prohibitive cost of running all available analyzers, organizations must run only a subset of all possible analyzers on their codebases. Choosing this set deterministically leaves recognizable gaps of vulnerability coverage. To overcome these challenges, we present Randomized Best Response (RBR), a method that computes an optimal randomization over size-bounded sets of available static analyzers. RBR models the relationship between malicious users and organizations as a leader-follower Stackelberg security game. Our solution focuses on software vulnerabilities due to their security implications when exploited by malicious users. Using 8 static analyzers for C/C++ and 8 Common Weakness Enumeration (CWE) vulnerability types, we show that RBR outperforms a set of natural baselines by always picking analyzers that achieve a higher benefit to the defender. Through a case study of a large system at Oracle, we show how RBR may be used in a real-world scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".