S1.E8: AI & Machine Learning in Testing | QA Therapy Podcast

Introduction

Welcome to the QA Therapy Podcast, where we explore ways to enhance testing and quality practices. In this episode, your hosts, Sergio Fre and Cristiano CA, discuss the implications of artificial intelligence (AI) and machine learning (ML) in the testing domain with special guest T. King, Vice President of Product Service Systems at EPAM H.

Understanding AI and Machine Learning

T. King begins by providing a clear definition of AI and ML. He describes AI as a broad field in computer science focused on simulating human intelligence, emphasizing cognitive functions like learning and reasoning. Machine learning, as a subset of AI, involves programming models that learn from data instead of relying on explicit instructions — meaning machines can infer functions based on given input-output examples.

The Rise of AI and ML

Despite AI and ML being present since the 1950s, the recent surge in interest can be attributed to three primary factors:

1. Data Ubiquity: The vast availability of data from various sources, including the internet and mobile devices.
2. Improved Compute Power: Advances in cloud computing and parallel processing capabilities.
3. Progress in Algorithms: Innovations like deep learning and neural networks enable machines to process large amounts of data more efficiently.

Role of Testers in the Age of AI/ML

King stresses the importance of understanding the level of AI and ML knowledge required for testers, depending on their role in the lifecycle. While some testers can effectively use AI-powered tools without deep technical expertise, others involved in the development and validation of AI systems will need a more comprehensive understanding.

Measuring Success in AI Testing

Unlike traditional testing where results are often definitive, AI and ML systems present challenges in defining success due to their inherently fuzzy logic. Success in these systems is predicated on how well a machine's outputs align with expected behaviors, a concept complicated by the unexplainable nature of some ML models. King highlights the importance of explainability in AI systems, particularly when outputs are ambiguous.

Data Preparation and Testing AI Systems

Effective preparation and validation of the training data are crucial in AI. Testers have a vital role in data selection, ensuring its quality and fairness, as well as in validating how the models behave with unseen data. They also need to consider how these systems operate in production since AI often continues to learn post-deployment.

Common Challenges in AI Testing

The conversation touches on several prevalent issues, including:

• Fairness: Ensuring that AI systems don't exacerbate existing biases.
• Overfitting: Maintaining a balance in training data to avoid skewed results.
• Integration: Challenges that arise when disparate AI systems need to work together.

Opportunities for Testers

AI and ML can significantly streamline the testing process through automation, from test case generation to analyzing vast datasets for actionable insights. King mentions the potential for testers to transition into roles that involve machine learning engineering, data validation, and cultural awareness in AI systems.

Conclusion

As AI and ML technologies evolve, testers are encouraged to adapt and embrace these changes. By participating actively in the development of AI systems, testers can play a pivotal role in shaping the future of quality assurance.

Vitamins for Your Testing Vitamins: X-ray and AI Tools

Many testing tools are leveraging AI, such as Functionize and Applitools. X-ray documentation showcases how to use them collaboratively to improve testing outcomes.

In a data-centric world, AI and ML offer intelligent ways to process information and draw valuable insights. We are at the forefront of an exciting era where smart collaborative testing processes will enable tools to handle a substantial portion of the testing workload—with testers guiding and validating results.

Keywords

• Artificial Intelligence
• Machine Learning
• Testing
• Automation
• Explainability
• Data Preparation
• Fairness
• Overfitting
• Integration Challenges
• Data Ubiquity
• Compute Power

FAQ

Q: What is the difference between AI and machine learning? A: AI is a broad field focused on simulating human intelligence, while machine learning is a subset of AI that involves models that learn from data rather than relying on explicit programming instructions.

Q: Why is AI and ML currently trending? A: The rise in AI and ML has been driven by the availability of vast data sources, increased computing power, and advances in algorithms, particularly deep learning.

Q: Do testers need to understand AI and ML deeply? A: It depends on their role. Some testers can effectively use AI tools without deep technical knowledge, while others involved in the development and validation processes may require a more comprehensive understanding.

Q: What are common challenges in testing AI systems? A: Common challenges include ensuring fairness, preventing overfitting, and addressing integration issues between different AI systems.

Q: How can AI improve the testing process? A: AI can streamline testing through automation, enabling tasks like test case generation and data analysis, thus allowing testers to focus more on oversight and strategic decision-making.