Directed or Undirected: Investigating Fuzzing Strategies in a CI/CD Setup (Registered Report)
Bibliographic record
Abstract
Fuzzing best practices suggest that fuzzing should be run for at least 24 hours, if not longer. This recommendation makes it hard to integrate fuzzing into CI/CD contexts, to rapidly check a commit for bugs. Existing studies on CI/CD fuzzing simulated a CI/CD environment by running undirected fuzzers on Magma benchmark programs, which have multiple bugs injected into a single version of the program. Directed fuzzers, such as AFLGo, aim to generate inputs that reach specific target locations in the program being fuzzed. Thus, they should be more effective at fuzzing in a CI/CD environment. In this study, we propose to evaluate both directed and undirected fuzzers in a simulated CI/CD environment. Like prior work, we will use Magma as a source of benchmarks, and run fuzzers for 10 minutes. Unlike prior work, we will start the fuzzing process from a saturated corpus, rather than Magma's default corpus. Also unlike prior work, we will run the fuzzers on versions of Magma programs with a single bug injected. To deal with the threat that Magma patches give directed fuzzers access to too precise information as to the bug location, we will also conduct experiments where we add additional lines of target code, to evaluate the sensitivity of directed fuzzers. Our registered report gives preliminary results on a small subset of benchmarks.
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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".