Enhancing Adaptive Test Healing with Graph Neural Networks for Dependency-Aware Decision Making
Bibliographic record
Abstract
Flaky tests are a major obstacle in modern CI/CD pipelines, leading to unreliable feedback, increased reruns, and developer frustration. Our previously published adaptive healing framework combined Large Language Models (LLMs) and Reinforcement Learning (RL) to automate flaky test recovery, but it assumed test independence and failed to account for structural dependencies between tests. In this paper, we introduce a significant extension to that baseline: a Graph Neural Network (GNN)-based Test Dependency Mapping layer that models intertest relationships. By integrating GNN embeddings with LLM-classified failures, the RL agent becomes dependency-aware, enabling more precise and efficient healing decisions. We evaluate the enhanced framework on a real-world industrial platform, a social lifestyle application actively used by thousands of users for health, nutrition, and coaching. Results show a 90% reduction in flaky test-related costs and faster, autonomous resolution of dependency-induced failures.
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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 | 0.001 | 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".