Enhancing Valid Test Input Generation with Distribution Awareness for Deep Neural Networks
Bibliographic record
Abstract
Comprehensive testing is important in improving the reliability of Deep Learning (DL)-based systems. Various Test Input Generators (TIGs) have been proposed to generate misbehavior-inducing test inputs. However, the lack of validity checking in TIGs often results in the generation of invalid inputs (i.e., out of the learned distribution), leading to unreliable testing. To save the effort of manually checking the validity and improve test efficiency, it is important to assess the effectiveness and reliability of automated validators. In this study, we comprehensively assess four automated Input Validators (IV s), Our findings show that the accuracy of IVs ranges from 49% to 77%. Distance-based IVs generally outperform reconstruction-based and density-based IVs for both classification and regression tasks. Based on the findings, we enhance existing testing frameworks by incorporating distribution awareness through joint optimization. The results demonstrate our framework leads to a 2 % to 10% increase in the number of valid inputs, which establishes our method as an effective technique for valid test input generation.
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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 | 0.000 |
| 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".