Skip to content

Instantly share code, notes, and snippets.

@textarcana

textarcana/qa_hf_blogs.md

Last active October 11, 2025 21:05
Show Gist options
  • Save textarcana/7ea04885744d85b4eb8f09528f017638 to your computer and use it in GitHub Desktop.
Save textarcana/7ea04885744d85b4eb8f09528f017638 to your computer and use it in GitHub Desktop.
Download ZIP
AI QA huggingface blog and medium posts
Raw
qa_hf_blogs.md

AI, NLP, and Hugging Face in Software Quality Assurance

This document lists blog posts and articles exploring the use of AI, NLP, and Hugging Face models in software quality assurance (QA) and software testing contexts.

📚 Blog Posts and Articles

1. Using NLP to Auto-Generate User Stories and Test Scenarios

Explores how NLP can help automatically generate user stories and test scenarios from unstructured inputs, reducing manual effort in QA.
[Using NLP to Auto-Generate User Stories and Test Scenarios]: https://medium.com/%40samareshmaiti02/using-nlp-to-auto-generate-user-stories-and-test-scenarios-a-leap-forward-in-software-quality-fbef0160f90d?utm_source=chatgpt.com

2. Understanding, Testing, Fine‑Tuning AI Model with HuggingFace

Covers aspects of validating and testing models within the Hugging Face ecosystem, focusing on fine-tuning and evaluation.
[Understanding, Testing, Fine‑Tuning AI Model with HuggingFace]: https://medium.com/executeautomation/understanding-testing-fine-tuning-ai-model-with-huggingface-4a4b9b342558?utm_source=chatgpt.com

3. AI and ML in Quality Assurance – The QA Revolution

Discusses how AI/ML are transforming QA practices, including automation, predictive QA, and practical applications.
[AI and ML in Quality Assurance – The QA Revolution]: https://medium.com/%40suhast_40578/ai-and-ml-in-quality-assurance-c98c98840a61?utm_source=chatgpt.com

4. How To Use Generative AI in Quality Assurance?

Focuses on how generative AI / NLP can assist QA — e.g., in improving test scripts, generation, and coverage analysis.
[How To Use Generative AI in Quality Assurance?]: https://www.olivesystems.co.il/blog/how-to-use-ai-in-quality-assurance?utm_source=chatgpt.com

5. How to Use AI in QA Software Testing: A Guide with Live OpenAI Demo

A practical guide on applying AI for software QA/testing with live demos.
[How to Use AI in QA Software Testing: A Guide with Live OpenAI Demo]: https://geekyants.com/en-us/blog/how-to-use-ai-in-qa-software-testing--a-guide-with-live-openai-demo?utm_source=chatgpt.com

6. AI in Software Testing: Your Guide to GenAI-Powered QA

A survey of how generative AI (GenAI) is integrated into testing and QA pipelines.
[AI in Software Testing: Your Guide to GenAI-Powered QA]: https://startups.epam.com/blog/ai-and-qa-process?utm_source=chatgpt.com

7. Revolutionizing Software Testing with Artificial Intelligence

Discusses how AI techniques such as smart crawling and pattern detection can enhance software testing workflows.
[Revolutionizing Software Testing with Artificial Intelligence]: https://www.launchableinc.com/blog/software-testing-with-artificial-intelligence/?utm_source=chatgpt.com

8. Quality Assurance Strategies for Machine Learning Applications

Research article on QA for ML models, addressing validation, robustness, and testing methodologies.
[Quality Assurance Strategies for Machine Learning Applications]: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-024-01028-y?utm_source=chatgpt.com

9. What CI/CD Practitioners Know That ML Engineers Don’t… Yet

Explains how QA and continuous integration principles apply to machine learning pipelines and model validation.
[What CI/CD Practitioners Know That ML Engineers Don’t… Yet]: https://huggingface.co/blog/Manialgie/what-cicd-practitioners-know-that-ml-engineers-don?utm_source=chatgpt.com

🔗 Reference Links

[Using NLP to Auto-Generate User Stories and Test Scenarios]
[Understanding, Testing, Fine‑Tuning AI Model with HuggingFace]
[AI and ML in Quality Assurance – The QA Revolution]
[How To Use Generative AI in Quality Assurance?]
[How to Use AI in QA Software Testing: A Guide with Live OpenAI Demo]
[AI in Software Testing: Your Guide to GenAI-Powered QA]
[Revolutionizing Software Testing with Artificial Intelligence]
[Quality Assurance Strategies for Machine Learning Applications]
[What CI/CD Practitioners Know That ML Engineers Don’t… Yet]

Raw
qa_hf_more.md

Additional AI Models and Techniques in Software Testing and QA

This document lists additional models, techniques, and frameworks currently used or studied for software testing and quality assurance (QA) beyond the ones previously covered.

🔍 Model‑Based Approaches

1. MuTAP: Mutation‑Guided Test Generation with Large Models

Uses mutation testing feedback to guide LLMs toward generating more effective test cases that reveal faults.
[MuTAP: Mutation‑Guided Test Generation with Large Models]: https://huggingface.co/papers/2308.16557?utm_source=chatgpt.com

2. Copilot for Testing (Context‑Based RAG + LLMs)

Applies retrieval‑augmented generation (RAG) to automatically detect bugs and generate tests within the IDE context.
[Copilot for Testing (Context‑Based RAG + LLMs)]: https://arxiv.org/abs/2504.01866?utm_source=chatgpt.com

3. CodeGemma Series (Google)

Family of code‑specialized language models capable of code completion, test generation, and reasoning about code quality.
[CodeGemma Series (Google)]: https://huggingface.co/google/codegemma-7b?utm_source=chatgpt.com

4. DeepMutation / DL‑based Mutation Testing

Applies deep learning models to select or rank mutants for evaluating the adequacy of test suites.
[DeepMutation / DL‑based Mutation Testing]: https://arxiv.org/abs/1905.13025?utm_source=chatgpt.com

5. Diffblue Cover (Java Test Generation Model)

Commercial model using symbolic reasoning and ML to automatically generate JUnit tests for Java codebases.
[Diffblue Cover (Java Test Generation Model)]: https://www.diffblue.com/?utm_source=chatgpt.com

📚 Broader Techniques & Research Directions

6. AI for Test Case Generation and Prioritization

Machine learning models used to create and prioritize test cases based on code complexity, history, or coverage.
[AI for Test Case Generation and Prioritization]: https://www.researchgate.net/publication/374263724_Artificial_Intelligence_in_Software_Testing_A_Systematic_Review?utm_source=chatgpt.com

7. Defect Prediction and Fault Localization

Supervised ML and deep learning methods predict buggy files, modules, or lines to guide targeted testing.
[Defect Prediction and Fault Localization]: https://arxiv.org/pdf/2409.02693?utm_source=chatgpt.com

8. Self‑Healing and Autonomous Tests

AI agents detect changes in UI or API structure and automatically repair broken tests in real‑time.
[Self‑Healing and Autonomous Tests]: https://www.rainforestqa.com/blog/ai-testing-tools?utm_source=chatgpt.com

9. Log Analysis and Anomaly Detection

LLMs and clustering models parse logs, classify test failures, and identify novel anomalies automatically.
[Log Analysis and Anomaly Detection]: https://pmc.ncbi.nlm.nih.gov/articles/PMC11668792/?utm_source=chatgpt.com

10. Search‑Based Software Testing (SBST) and Evolutionary Algorithms

Combines ML with genetic algorithms to evolve effective test suites. Tools like EvoSuite are examples.
[Search‑Based Software Testing (SBST) and Evolutionary Algorithms]: https://en.wikipedia.org/wiki/EvoSuite?utm_source=chatgpt.com

11. Test and Evaluation of AI Systems (DoD Framework)

Framework for testing and verifying the safety, robustness, and performance of AI/ML models in critical systems.
[Test and Evaluation of AI Systems (DoD Framework)]: https://www.ai.mil/Portals/137/Documents/Resources%20Page/Test%20and%20Evaluation%20of%20Artificial%20Intelligence%20Models%20Framework.pdf?utm_source=chatgpt.com

12. Automated Patch and Bug‑Repair Models (e.g., CodeT5+, RepairCoder)

Code‑aware transformer models that automatically propose code patches or bug fixes, which can also inform regression testing.
[Automated Patch and Bug‑Repair Models (e.g., CodeT5+, RepairCoder)]: https://huggingface.co/Salesforce/codet5p-770m-py?utm_source=chatgpt.com

🧠 Experimental / Frontier Topics

13. LLM‑Based Program Understanding and QA Agents

Multi‑agent frameworks where one model generates tests, another reviews, and a third executes / reasons about outputs.
[LLM‑Based Program Understanding and QA Agents]: https://arxiv.org/abs/2405.09000?utm_source=chatgpt.com

14. Visual / UI Regression QA with Vision‑Language Models

Using VLMs to check whether rendered UIs match specifications or detect layout regressions.
[Visual / UI Regression QA with Vision‑Language Models]: https://huggingface.co/papers/2505.15952?utm_source=chatgpt.com

15. Autonomous QA Assistants (BugBlitz‑AI, TestGPT)

LLMs fine‑tuned on QA artifacts (bug reports, test plans) that assist human testers by classifying and summarizing issues.
[Autonomous QA Assistants (BugBlitz‑AI, TestGPT)]: https://arxiv.org/abs/2406.04356?utm_source=chatgpt.com

Raw
qa_hf_white_papers.md

White Papers and Research on AI in Software Quality Assurance

This document lists white papers, technical reports, and academic papers exploring how AI, ML, NLP, and Hugging Face–style models are being applied to software testing and quality assurance (QA).

📄 White Papers & Technical Reports

1. AI and Software Testing — Building Systems You Can Trust

A Keysight white paper exploring trust, complexity, and testability in AI-driven systems.
[AI and Software Testing — Building Systems You Can Trust]: https://www.keysight.com/us/en/assets/3123-1299/white-papers/AI-and-Software-Testing-Building-Systems-You-Can-Trust.pdf?utm_source=chatgpt.com

2. AI Whitepaper — The New Wave Transforming Testing Landscape

Conformiq’s white paper on how AI is reshaping testing practices and automation adoption.
[AI Whitepaper — The New Wave Transforming Testing Landscape]: https://www.conformiq.com/wp-content/uploads/2022/07/AI-whitepaper_The-new-wave-transforming-testing-landscape.pdf?utm_source=chatgpt.com

3. AI in Testing (SAPinsider)

A high-level view of how AI/ML can be used for test-case generation, fault detection, and optimization.
[AI in Testing (SAPinsider)]: https://sapinsider.org/wp-content/uploads/2025/01/White-paper-AI.pdf?utm_source=chatgpt.com

4. Reducing Software Testing Timelines and Cost with AI and ML

Qualitest white paper describing practical applications of AI for reducing QA costs and accelerating pipelines.
[Reducing Software Testing Timelines and Cost with AI and ML]: https://www.qualitestgroup.com/insights/white-paper/reducing-software-testing-timelines-and-cost-with-ai-and-ml/?utm_source=chatgpt.com

5. AI Testing Framework: Building Trustworthy AI Systems

A TCS white paper proposing frameworks for testing AI systems, focusing on explainability, bias, and reliability.
[AI Testing Framework: Building Trustworthy AI Systems]: https://www.tcs.com/what-we-do/research/white-paper/ai-testing-framework-build-trustworthy-systems?utm_source=chatgpt.com

6. Embracing Generative AI in Software Testing

Persistent Systems white paper on integrating generative AI into enterprise QA strategies.
[Embracing Generative AI in Software Testing]: https://www.persistent.com/wp-content/uploads/2024/03/whitepaper-embracing-generative-ai-in-software-testing.pdf?utm_source=chatgpt.com

7. The Role of AI in Testing: Blueprint for Smart Test Automation Success

ACCELQ white paper describing how to embed AI into testing workflows and mitigate risk.
[The Role of AI in Testing: Blueprint for Smart Test Automation Success]: https://www.accelq.com/white-paper/role-of-ai-in-testing/?utm_source=chatgpt.com

8. Guide for AI in Software Testing

Parasoft’s guide on enhancing testing (unit, API, static analysis) using AI.
[Guide for AI in Software Testing]: https://www.parasoft.com/white-paper/guide-for-ai-in-software-testing/?utm_source=chatgpt.com

9. Artificial Intelligence and the Testing Industry: A Primer

ATP white paper offering an industry-wide perspective on the role of AI in testing.
[Artificial Intelligence and the Testing Industry: A Primer]: https://www.testpublishers.org/assets/ATP%20White%20Paper_AI%20and%20Testing_A%20Primer_6July2021_Final%20R1%20.pdf?utm_source=chatgpt.com

10. The Impact of Artificial Intelligence on Software Testing and Mock Data Generation

A Testrig Technologies paper on AI-driven test data generation and mocking strategies.
[The Impact of Artificial Intelligence on Software Testing and Mock Data Generation]: https://www.testrigtechnologies.com/wp-content/uploads/2024/09/Impact-AI-WhitePaper.pdf?utm_source=chatgpt.com

11. Using Image Compare with Computer Vision for Testing

FIS white paper on applying computer vision and AI to UI and visual regression testing.
[Using Image Compare with Computer Vision for Testing]: https://www.fisglobal.com/-/media/fisglobal/files/pdf/white-paper/ai-image-compare-white-paper.pdf?utm_source=chatgpt.com

12. Proxy Validation and Verification for Critical AI Systems

NIST report on validation and verification methods for high-stakes AI systems.
[Proxy Validation and Verification for Critical AI Systems]: https://nvlpubs.nist.gov/nistpubs/CSWP/NIST.CSWP.31.pdf?utm_source=chatgpt.com

📚 Academic / Research Papers

Quality Assurance for Artificial Intelligence: A Study of Industrial Concerns, Challenges and Best Practices

Analyzes QA practices for AI systems in industry through interviews and surveys.
[Quality Assurance for Artificial Intelligence: A Study of Industrial Concerns, Challenges and Best Practices]: https://arxiv.org/abs/2402.16391?utm_source=chatgpt.com

AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions

Evaluates how AI-driven tools are impacting modern QA and testing workflows.
[AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions]: https://arxiv.org/abs/2506.16586?utm_source=chatgpt.com

BugBlitz‑AI: An Intelligent QA Assistant

Proposes an AI-based QA assistant for automating result analysis and bug reporting.
[BugBlitz‑AI: An Intelligent QA Assistant]: https://arxiv.org/abs/2406.04356?utm_source=chatgpt.com

The Role of Artificial Intelligence and Machine Learning in Software Testing

A survey of AI/ML methods applied to software testing and defect prediction.
[The Role of Artificial Intelligence and Machine Learning in Software Testing]: https://arxiv.org/abs/2409.02693?utm_source=chatgpt.com

🔗 Reference Links

[AI and Software Testing — Building Systems You Can Trust]
[AI Whitepaper — The New Wave Transforming Testing Landscape]
[AI in Testing (SAPinsider)]
[Reducing Software Testing Timelines and Cost with AI and ML]
[AI Testing Framework: Building Trustworthy AI Systems]
[Embracing Generative AI in Software Testing]
[The Role of AI in Testing: Blueprint for Smart Test Automation Success]
[Guide for AI in Software Testing]
[Artificial Intelligence and the Testing Industry: A Primer]
[The Impact of Artificial Intelligence on Software Testing and Mock Data Generation]
[Using Image Compare with Computer Vision for Testing]
[Proxy Validation and Verification for Critical AI Systems]
[Quality Assurance for Artificial Intelligence: A Study of Industrial Concerns, Challenges and Best Practices]
[AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions]
[BugBlitz‑AI: An Intelligent QA Assistant]
[The Role of Artificial Intelligence and Machine Learning in Software Testing]

Raw
testing_llm_sota.md

Blogs and Posts: Using LLMs to Generate High-Level Test Scenarios for Software Testers

This document compiles blogs, articles, and case studies describing how software testers can leverage Large Language Models (LLMs) to generate high-level test scenarios, test ideas, and acceptance tests.

📰 Blog Posts & Articles

1. LLM‑Prompts that will take you From Zero To Hero in Software Testing

Discusses prompt crafting strategies for moving from requirement text to test cases and ideas; includes reusable prompts for testers.
[LLM‑Prompts that will take you From Zero To Hero in Software Testing]: https://medium.com/%40monish.correia/llm-prompts-that-will-take-you-from-zero-to-hero-in-software-testing-74d73e0a411f?utm_source=chatgpt.com

2. LLM‑Powered Test Case Generation: Enhancing Coverage and Efficiency

Explains how LLMs can be used for test generation, mutation testing, and refining test coverage via iterative prompting.
[LLM‑Powered Test Case Generation: Enhancing Coverage and Efficiency]: https://www.frugaltesting.com/blog/llm-powered-test-case-generation-enhancing-coverage-and-efficiency?utm_source=chatgpt.com

3. Building AI Agents to Automate Software Test Case Creation (NVIDIA)

Introduces NVIDIA’s HEPH framework, which uses LLM agents to extract requirements, maintain traceability, and generate context-aware test cases.
[Building AI Agents to Automate Software Test Case Creation (NVIDIA)]: https://developer.nvidia.com/blog/building-ai-agents-to-automate-software-test-case-creation/?utm_source=chatgpt.com

4. AI‑Generated Test Cases from User Stories (ThoughtWorks Research)

Empirical comparison between manual and AI-generated test cases; includes evaluation metrics and best practices.
[AI‑Generated Test Cases from User Stories (ThoughtWorks Research)]: https://www.thoughtworks.com/en-us/insights/blog/generative-ai/AI-generated-test-cases-from-user-stories-an-experimental-research-study?utm_source=chatgpt.com

5. Software Testing: Using Large Language Models to Save Time (Fraunhofer IESE)

Explores structured test case generation from requirements, comparing different models and evaluation metrics.
[Software Testing: Using Large Language Models to Save Time (Fraunhofer IESE)]: https://www.iese.fraunhofer.de/blog/software-testing-test-case-generation-using-ai-llm/?utm_source=chatgpt.com

6. Implementing AI in Software Testing: Creating a Text‑Generation Model for Test Automation

Hands-on tutorial showing how to build a text-generation system that converts requirements into test scenarios and automation-ready test data.
[Implementing AI in Software Testing: Creating a Text‑Generation Model for Test Automation]: https://drlee.io/implementing-ai-in-software-testing-creating-a-text-generation-model-for-test-automation-7294b26f93c4?utm_source=chatgpt.com

7. How LLMs are Powering Automated Test Case and Data Generation

Explains how LLMs can generate test scenarios, test data, and expected outcomes together to improve end-to-end coverage.
[How LLMs are Powering Automated Test Case and Data Generation]: https://community.snaplogic.com/blog/sl-tech-blog/revolutionizing-software-testing-how-llms-are-powering-automated-test-case-and-d/40104?utm_source=chatgpt.com

8. Assessing ChatGPT & LLMs for Software Testing (Xray Blog)

Evaluates benefits and limitations of LLMs for QA work; highlights how to review and validate generated test scenarios.
[Assessing ChatGPT & LLMs for Software Testing (Xray Blog)]: https://www.getxray.app/blog/chatgpt-llms-software-testing?utm_source=chatgpt.com

🔍 Key Practices & Takeaways for Testers

  1. Prompt Engineering is Crucial — Quality of generated scenarios depends heavily on how the LLM is prompted. Provide structure: “Given the user story, list high-level test scenarios with preconditions, steps, and expected outcomes.”

  2. Iterative Refinement — Review outputs, then adjust prompts by adding context, examples, or acceptance criteria to improve alignment.

  3. Human Review is Non‑Optional — Testers must validate and refine outputs. LLMs may hallucinate or misinterpret domain rules.

  4. Use Structured Inputs — Provide user stories, business rules, or acceptance criteria as clearly separated fields to help LLMs parse intent.

  5. Generate Test Data and Scenarios Together — Combining both yields richer coverage and helps surface edge cases.

  6. Maintain Traceability — Link each generated test scenario back to its original requirement to ensure coverage and accountability.

  7. Evaluate Quality with Metrics — Track coverage, redundancy, and correctness of LLM‑generated test cases to benchmark their value.

  8. Combine Generation + Prioritization — Generate many scenarios and then prioritize using heuristics (risk, frequency, or criticality).

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment