Leveraging Documentation to Test Deep Learning Library Functions
Bibliographic record
Abstract
It is integral to test API functions of widely used deep learning (DL) libraries. The effectiveness of such testing requires DL specific input constraints of these API functions. Such constraints enable the generation of valid inputs, i.e., inputs that follow these DL specific constraints, to explore deep to test the core functionality of API functions. Existing fuzzers have no knowledge of such constraints, and existing constraint extraction techniques are ineffective for extracting DL specific input constraints. To fill this gap, we design and implement a document guided fuzzing technique, D2C, for API functions of DL libraries. D2C leverages sequential pattern mining to generate rules for extracting DL specific constraints from API documents and uses these constraints to guide the fuzzing to generate valid inputs automatically. D2C also generates inputs that violate these constraints to test the input validity checking code. In addition, D2C uses the constraints to generate boundary inputs to detect more bugs. Our evaluation of three popular DL libraries (TensorFlow, PyTorch, and MXNet) shows that D2C's accuracy in extracting input constraints is 83.3% to 90.0%. D2C detects 121 bugs, while a baseline fuzzer without input constraints detects only 68 bugs. Most (89) of the 121 bugs are previously unknown, 54 of which have been fixed or confirmed by developers after we report them. In addition, D2C detects 38 inconsistencies within documents, including 28 that are fixed or confirmed after we report them.
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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 | 0.008 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".