ENTERPRISE AI ANALYSIS
Automating Flaky Test Detection and Root-Cause Analysis in Quantum Software
Quantum software systems, like classical ones, depend on automated testing. However, their probabilistic nature makes them prone to "quantum flakiness"—tests that pass or fail inconsistently without code changes. These flaky tests can obscure real defects and hinder developer productivity. Our research introduces an automated pipeline leveraging Large Language Models (LLMs) to detect these issues and identify their root causes, enhancing the reliability and maintainability of quantum software.
Key Outcomes & Measurable Impact
Our innovative approach delivers significant advancements in quantum software quality assurance, providing actionable insights for developers and organizations.
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Original Dataset Size Increase
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Flakiness Detection F1-score
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Root-Cause Identification F1-score
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
25
New Flaky Tests Identified
Our pipeline successfully identified 25 previously unknown flaky tests, significantly expanding the dataset of quantum flaky test instances by 54% and providing valuable new data for analysis and model training.
Quantum Flakiness Detection Pipeline
Embed GitHub IRs/PRs
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Calculate Cosine Similarity
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Rank Issues by Similarity
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LLM Classification (Flaky/Non-Flaky)
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Identify New Flaky Tests
Automated Discovery with Embeddings: To systematically detect new flaky test cases, we employed embedding transformers to represent GitHub Issue Reports (IRs) and Pull Requests (PRs). By calculating the cosine similarity between these new entries and our existing dataset of known flaky tests, we could rank and identify potential new instances. This automated approach significantly increased the efficiency and scale of our data collection compared to manual methods.
Randomness
Dominant Root Cause of Flakiness
Our analysis of 71 quantum flaky tests revealed that 'Randomness' is the most common root cause, accounting for 19.2% of cases, often fixed by setting a pseudo-random number generator (PRNG) seed.
| Cause Category | Prevalence | Common Fix Pattern | Fix Prevalence (of category) |
|---|---|---|---|
| Randomness | 19.2% | Fix Seed | 16.4% (total fixes) / 85.7% (of category) |
| Multi-Threading | 13.7% | Make Single Thread | 6.8% (total fixes) / 50% (of category) |
| Software Env. | 11.0% | Alter Software Env. | 6.8% (total fixes) / 62.5% (of category) |
| Floating Point Ops. | 9.6% | Adjust Tolerance | 6.8% (total fixes) / 71.4% (of category) |
Example: Randomness in Qiskit
In qiskit issue #5217, the test_append_circuit function occasionally failed due to its reliance on random_circuit, which uses a randomly selected seed by default. The fix involved setting a constant seed (e.g., seed=4200) to ensure consistent circuit generation across test runs, eliminating the flakiness. This illustrates how even subtle probabilistic elements can introduce significant testing challenges in quantum software.
0.9643
Peak Root Cause Identification F1-Score
Google Gemini 2.5 Flash achieved the highest F1-score of 0.9643 for root-cause identification, demonstrating the strong capability of LLMs in diagnosing complex quantum software issues.
| Model | Task | Context | F1-score | MCC |
|---|---|---|---|---|
| Gemini 2.5 Flash | Flakiness Detection (RQ3) | {Rf, Cp} | 0.9420 | 0.8887 |
| Gemini 2.5 Flash | Root-Cause Identification (RQ5) | {Rf, Cf} | 0.9643 | 0.4769 |
| GPT-40 (2024-11-20) | Flakiness Detection (RQ3) | {Rf, Cp} | 0.8649 | 0.7209 |
Impact of Context on LLM Reasoning
Our experiments revealed that providing enriched context, particularly full descriptions with comments (Rf) and method-level code (Cp), significantly improved LLM performance across research questions. For instance, using Rf generally aided models in making better decisions, with performance drops being relatively small when an issue or pull request was initially opened. This highlights the importance of comprehensive textual and code context for effective automated analysis.
Calculate Your Potential AI ROI
Estimate the potential cost savings and efficiency gains your organization could achieve by automating test flakiness detection and root-cause analysis with our AI-powered solutions.
Estimated Annual Savings
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Developer Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating automated flaky test detection and root-cause analysis into your quantum software development lifecycle.
Phase 1: Discovery & Assessment
Conduct an in-depth analysis of your current testing infrastructure, identify key pain points related to flaky tests, and assess the suitability of AI integration. Define project scope and success metrics.
Phase 2: Data Preparation & Model Training
Gather and preprocess historical test data, including flaky test reports and associated code changes. Train and fine-tune LLMs on your specific quantum software codebase and testing patterns.
Phase 3: Pipeline Integration & Pilot Deployment
Integrate the automated detection pipeline into your CI/CD workflows. Deploy a pilot program with a select team or project to validate the system's effectiveness and gather initial feedback.
Phase 4: Optimization & Full Rollout
Refine the AI models and pipeline based on pilot results. Scale the solution across your organization, provide training to development teams, and establish ongoing monitoring and maintenance.
Ready to Transform Your Quantum Software Quality?
Automate flaky test detection and accelerate root-cause analysis with our specialized AI solutions. Schedule a consultation to explore how we can tailor our pipeline to your enterprise needs.
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