Significance of AI in Testing by Krishan Meghani

1. Significance of AI in Testing
Krishan
CEO & Founder MeghaAI
AI
Different Types
AI for Testing

2. Introduction
Increasing complexity of software applications and the need for efficient testing
methods
Unit Tests.
Integration Tests.
UI Tests.

3. What is an AI

4. Machine Learning

5. Deep Learning

6. Recap-Relationships

7. Recap-What problems should be solved
by AI ?
AI or No AI
Inputs Outputs

8. What Makes AI Relevant Today (1/2) ?

9. What Makes AI Relevant Today (2/2) ?
• You tube has 2 billion monthly users and 800M daily active users.
• 500 hours of video is uploaded per minute on you tube.
• Twitter has 145M daily active users with 500M tweets generated per day.
• Instagram has 500M daily users with 1000 photos generated per second.
• FB has 1.84 billion daily users, 184M uploading photos/texts.
• Swiggy clocks 1.4 millions food order daily.
• NetFlix has 200M subscribers watching with 164Millions hours per day being watched.

10. Personality Embeddings (1/3)

11. Personality Embeddings (2/3)

12. 1.We can represent people (and things) as vectors of numbers (which is great for machines!).
2.We can easily calculate how similar vectors are to each other.
Personality Embeddings (3/3)

13. What AI can do
Corelation between the parameters – How strong or weak are parameters
related ?
Predict ahead in the time - what is next value at time ‘t’ in the future ?
Predict the value from other values – this is like solving the equations ?
Find the problem in the data all together – anomalies – Example of credit
card transation
Predict / Generate the next word
Compare words, Statements and Paragraphs and documents

14. Generative AI
Text Based – GPT, Llama-2 ( Stories, Movies etc)
Image Based – DALL-E, Mid Journey

15. What is AI in Testing?
• AI in the context of software testing.
• How AI can mimic human-like testing activities, including test case generation, execution, and analysis.
Test Case Generation
Requirements
Test Analysis
Test Data
Test Execution
Mimic Users

16. Types of AI in Testing
• Different types of AI techniques used in testing, such as machine learning, natural language processing,
and computer vision.
• How each type can be applied in testing scenarios.
Machine Learning (ML) in Testing
Example: Anomaly Detection in Log Files
Natural Language Processing (NLP) in Testing:
Example: Test Case Natural Language Understanding
Computer Vision in Testing:
Example: UI Testing and Visual Regression Testing

17. Benefits of AI in Testing
• Improved test coverage
• Faster test execution
• Early defect detection
• Reduction in testing costs
• Enhanced test case generation
• Continuous testing capabilities

18. Challenges and Limitations
• Lack of domain knowledge
• Data quality issues
• Initial setup and training efforts
• Ethical concerns

19. Future Trends
• AI for test data generation
• AI-powered test automation
• AI-driven test prioritization
• AI in security testing

20. Best Practices
• Start with a clear strategy
• Select the right AI tools and technologies
• Invest in training and upskilling
• Continuously monitor and adapt your AI testing strategy

21. Ethical Considerations
1.Bias and Fairness:
1. Issue: AI models can inherit biases from training data, leading to unfair treatment of certain groups.
2. Mitigation: Regularly audit and retrain models to minimize biases. Use diverse and representative training data.
2.Privacy:
1. Issue: AI may inadvertently expose sensitive user data during testing.
2. Mitigation: Ensure that AI testing tools comply with data protection regulations (e.g., GDPR). Anonymize or
pseudonymize data whenever possible.
3.Transparency and Accountability:
1. Issue: AI decision-making can be opaque, making it challenging to understand how and why certain testing
outcomes occur.
2. Mitigation: Strive for transparency in AI algorithms and decision-making processes. Maintain clear documentation
of AI testing methodologies.
4.Data Privacy and Security:
1. Issue: Collecting, storing, and managing test data may expose vulnerabilities.
2. Mitigation: Implement robust data security measures, including encryption and access controls, to protect
sensitive testing data.
5.Job Displacement:
1. Issue: Widespread adoption of AI in testing may raise concerns about job displacement for manual testers.
2. Mitigation: Focus on upskilling testers to work alongside AI tools, emphasizing critical thinking, test strategy, and
ethical considerations.
6.Adherence to Regulations:
1. Issue: Failure to comply with industry-specific regulations and standards can lead to legal and financial
repercussions.
2. Mitigation: Stay informed about relevant industry regulations and ensure that AI testing practices align with
compliance requirements.
7.Algorithmic Accountability:
1. Issue: When AI systems make mistakes, it can be challenging to assign responsibility.

22. Conclusion
AI is a transformative force that is reshaping the way we ensure software quality
Embracing AI in testing is not just a choice; it's a strategic imperative for organizations seeking to deliver high-
quality software efficiently and competitively.