Day 2: Read an introductory article on AI in testing and share it
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Since Day1’s task was introducing ourselves and why we wanted to take part in the challenge. I directly began with Day2.
I read an introductory article on AI and ML for testers by Pricilla Billavendran
Here are my learnings from the article.
Introduction to AI and Machine Learning (ML)
- Artificial Intelligence (AI): Machines’ ability to perform tasks requiring human intelligence.
- Machine Learning (ML): A subset of AI where algorithms learn from data and improve over time without explicit programming.
- Origin: AI coined by John McCarthy (1955), ML by Arthur Samuel (1959).
Types of AI
- Narrow AI: Performs specific tasks (e.g., Siri, facial recognition).
- General AI: Matches human intelligence, can perform varied tasks (not yet achieved).
- Super AI: Exceeds human capabilities (theoretical, not yet realized).
Categories of AI by Functionality
- Reactive Machines AI: Reacts to inputs, doesn’t learn from the past.
- Limited Memory AI: Retains past data for decision-making.
- Theory of Mind AI: Simulates human understanding (still in development).
- Self-Awareness AI: Understands its existence and has self-reflection (theoretical).
Types of Machine Learning
- Supervised Learning: Algorithms learn from labeled training data.
- Unsupervised Learning: Algorithms find patterns in unlabeled data.
- Semi-Supervised Learning: Mix of labeled and unlabeled data.
- Reinforcement Learning: Agents learn through interaction with the environment and rewards.
AI and ML in Software Testing
- Automation: AI and ML can automate repetitive testing tasks.
- Defect Detection: AI/ML can identify potential coding defects.
- Test Case Generation: AI/ML can create test cases based on software requirements.
- Real-Time Feedback: AI/ML can provide instant feedback to testers.
- Anomaly Detection: AI/ML identifies unusual patterns or behaviors in software.
Benefits of AI and ML in Software Testing
- Improved Accuracy: AI/ML detects patterns that humans might miss.
- Increased Efficiency: AI/ML speeds up testing by automating tasks.
- Reduced Costs: Automation can lower overall testing costs.
- Improved Risk Management: AI/ML helps identify potential risks.
- Enhanced Data Analysis: AI/ML analyzes large datasets for insights.
Challenges of AI and ML in Software Testing
- Autonomicity: AI-driven testing might miss things requiring human intuition.
- Cost: Initial costs for AI/ML technology can be high.
- Bias: AI/ML can inherit biases from training data.
- Security: Protecting sensitive information is crucial.
- Complexity: Understanding AI/ML algorithms can be challenging.
Conclusion
- AI and ML offer significant potential in software testing but come with challenges.
- They should be used to augment, not replace, human testers.
- Testers should leverage AI/ML while applying their domain knowledge and creativity to ensure effective testing