The document discusses how AI/ML can support software testing now and in the future. It describes two main ways AI/ML can be adopted: through test tools with AI/ML supports or by building custom ML models. AI/ML can help with test case generation, natural language test specifications, visual testing through object recognition, autonomous test execution, and test maintenance. However, challenges remain around data quality, transparency, and the need for human interpretation. The conclusion is that AI/ML is a useful tool to help testing but testers are still needed and not fully replaced.

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AI/ML-powered
Software Testing -
Present & Future
Minh Nguyen
AI/ML enthusiast, Testify

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2 questions
1. How does AI/ML support testing – now and future?
2. Will testers be replaced by AI/ML-supported test tools?
Disclaimer:
• High development pace in AI/ML general and testing support.
• Personal point of view.

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Context
• Test automation – Efficiency (Rapid)
 traditional deterministic approach: input + rules = output
• AI/ML supported Test – Effectiveness (Smart)
 Non-deterministic approach: input + output = rules

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Implementation approaches
2 ways to adopt ML:
1. Test tools with AI/ML supports
2. Self-built ML model
a) AI/ML services (MS Azure, AWS AI, Google AI, OpenAI, IBM Watson, …)
b) Open-source library (TensorFlow, PyTorch, Keras, ...)

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AI-supported test tool
Test tools with AI support categorized in:
1. Test case generation
2. Natural language test specification
3. Visual testing by object recognition (Computer Vision)
4. Autonomous test execution (Coverage, Bug-hunting, Usage pattern)
5. Test maintenance (GUI object recognition; test modification after changes)
1 2 3 4 5

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AI-supported test process
• Requirement Risk Prediction
• Bug Category Clustering
• Test Execution Time Prediction

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AI-supported test process
• End-user Usage Pattern Prediction
• Requirement Testability Classification

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AI-supported test process
• (Synthetical) Test Data Generation
• Test Environment Selection

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AI-supported test process
• Bug Cause Prediction
• Bug Location Prediction
• Bug Correction Time Prediction

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Challenges
• Training data (availability, quality, creditability, …)
• Trust (in algorithm we trust...)
• Transparency (what is rationale behind?)
• Still need manual interpretation and analysis

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Conclusions
1. AI/ML is useful tool for effective and smart testing  co-pilot to
human tester.
2. AI/ML still not fully covered all test activities  tester is still
needed