Test Generation Strategies for Building Failure Models and Explaining Spurious Failures
Bibliographic record
Abstract
Test inputs fail not only when the system under test is faulty but also when the inputs are invalid or unrealistic. Failures resulting from invalid or unrealistic test inputs are spurious. Avoiding spurious failures improves the effectiveness of testing in exercising the main functions of a system, particularly for compute-intensive (CI) systems where a single test execution takes significant time. In this article, we propose to build failure models for inferring interpretable rules on test inputs that cause spurious failures. We examine two alternative strategies for building failure models: (1) machine learning (ML)-guided test generation and (2) surrogate-assisted test generation. ML-guided test generation infers boundary regions that separate passing and failing test inputs and samples test inputs from those regions. Surrogate-assisted test generation relies on surrogate models to predict labels for test inputs instead of exercising all the inputs. We propose a novel surrogate-assisted algorithm that uses multiple surrogate models simultaneously, and dynamically selects the prediction from the most accurate model. We empirically evaluate the accuracy of failure models inferred based on surrogate-assisted and ML-guided test generation algorithms. Using case studies from the domains of cyber-physical systems and networks, we show that our proposed surrogate-assisted approach generates failure models with an average accuracy of 83%, significantly outperforming ML-guided test generation and two baselines. Further, our approach learns failure-inducing rules that identify genuine spurious failures as validated against domain knowledge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".