CEDAR: Continuous Testing of Deep Learning Libraries
Bibliographic record
Abstract
Since Deep Learning (DL) libraries undergo rapid development with thousands of lines of code changes daily, they require continuous testing to detect software bugs and ensure code quality. In this paper, we explore DL testing approaches in a continuous testing setting. To make it feasible, we present the first continuous testing framework for DL libraries-CEDAR-that integrates two state-of-the-art DL testing approaches (DocTer and EAGLE) efficiently to test two popular DL libraries, PyTorch and TensorFlow. Through the application of CEDAR to 20 versions of PyTorch and TensorFlow, CEDAR detects 83 bugs in 140 APIs. Out of the 83 bugs, 23 are previously unknown bugs with 21 confirmed or fixed by the developers. The results also show CEDAR has effectively shortened the bug detection latency by almost a year (338.6 days) on average. In addition, CEDAR demonstrates its effectiveness in detecting new regression bugs and masked bugs. With three optimization strategies, CEDAR reduces the time and space overhead by a factor of 15.4 and 9.7. We share insights and lessons learned from our research, aiming to advance the development of more effective and efficient continuous testing for DL libraries, benefiting both developers and researchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".