Bibliographic record
Abstract
Software engineers inadvertently introduce bugs into software during the development process and these bugs can potentially be exploited once the software is deployed. As the size and complexity of software systems increase, it is important that we are able to verify and validate not only that the software behaves as it is expected to, but also that it does not violate any security policies or properties. One of the approaches to reduce software vulnerabilities is to use a bug detection tool during the development process. Many bug detection techniques are limited by the burdensome and error prone process of manually writing a bug specification. Other techniques are able to learn specifications from examples, but are limited in the types of bugs that they are able to discover. This work presents a novel, general approach for synthesizing security rules for C code. The approach combines human knowledge with an interactive logic programming synthesis system to learn Datalog rules for various security properties. The approach has been successfully used to synthesize rules for three intraprocedural security properties: (1) out of bounds array accesses, (2) return value validation, and (3) double freed pointers. These rules have been evaluated on randomly generated C code and yield a 0% false positive rate and a 0%, 20%, and 0% false negative rate, respectively for each rule.--Author's abstract
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".