CoCoFuzzing: Testing Neural <u>Co</u>de Models With <u>Co</u>verage-Guided <u>Fuzzing</u>
Bibliographic record
Abstract
Deep learning (DL)-based code processing models have demonstrated good performance for tasks such as method name prediction, program summarization, and comment generation. However, despite the tremendous advancements, DL models are frequently susceptible to adversarial attacks, which pose a significant threat to the robustness and generalizability of these models by causing them to misclassify unexpected inputs. To address the issue above, numerous DL testing approaches have been proposed; however, these approaches primarily target testing DL applications in the domains of image, audio, and text analysis, etc., and cannot be “directly applied” to “neural models for code” due to the unique properties of programs. In this article, we propose a coverage-based fuzzing framework,CoCoFuzzing, for testing DL-based code processing models. In particular, we first propose 10 mutation operators to automatically generate validly and semantically preserving source code examples as tests, followed by a neuron coverage (NC)-based approach for guiding the generation of tests. The performance ofCoCoFuzzingis evaluated using three state-of-the-art neural code models, i.e., NeuralCodeSum, CODE2SEQ, and CODE2VEC. Our experiment results indicate thatCoCoFuzzingcan generate validly and semantically preserving source code examples for testing the robustness and generalizability of these models and enhancing NC. Furthermore, these tests can be used for adversarial retraining to improve the performance of neural code models.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".