Artificial intelligence for faster and smarter software testing - Galway Meetup May 9th
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The document discusses the transformative impact of artificial intelligence (AI) on software testing, highlighting its influence on business processes and software quality. Key benefits of AI in testing include intelligent test design, maintenance, and execution, which improve speed, scalability, and customer satisfaction. It also outlines SmartBear's initiatives to integrate AI into testing workflows, enabling more efficient and effective testing practices.
Artificial intelligence for faster and smarter software testing - Galway Meetup May 9th
- 1. Artificial Intelligence for Faster and Smarter Software Testing
- 2. Joseph JoyceDermot Canniffe Proprietary & Confidential Your Team Today! 2 Lorna Smyth
- 3. How is Artificial Intelligence (AI) transforming businesses? How can AI impact Software Quality? What areas is AI impacting software testing the most? What is SmartBear doing with AI to help testers? Key Questions to Consider Proprietary & Confidential 3
- 4. How many people are using AI in their day-to-day lives? Proprietary & Confidential 4
- 5. AI-Driven technology is reshaping our day-to-day lives, changing the way we see the world. Proprietary & Confidential 5
- 6. …personalizing content, making our lives easier through email responses Proprietary & Confidential 6
- 7. …providing us detailed information with chat bots Proprietary & Confidential 7
- 8. …driving purchase decisions from targeted ads Proprietary & Confidential 8
- 9. ….transforming Healthcare Proprietary & Confidential 9
- 10. Imagine using AI to explore 1000 possible application designs from a list of requirements. Proprietary & Confidential 10 Autodesk Dreamcatcher AI: The Next Generation of CAD
- 11. M87 – Black Hole Proprietary & Confidential 11 Continuous High-resolution Image Reconstruction using Patch priors – CHIRP • Bayesian Algorithm • Compensates for Low Signal/Noise Ratio in Very long baseline interferometry Katherine L. Bouman, Michael D. Johnson, Daniel Zoran, Vincent L. Fish, Sheperd S. Doeleman, William T. Freeman; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 913-922
- 12. What is AI? Neural networks Machine learning Deep learning or the networks of hardware and software that approximate the web of neurons in the human brain which is a technique using algorithms to teach machines to learn which helps machines learn to go deeper into data to recognize patterns Proprietary & Confidential 12 AI has come to represent the broad category of methodologies that teach a computer to perform tasks as an “intelligent” person would
- 13. We Want Faster Release Cycles Proprietary & Confidential 13 Automation Capability High Agile Testing Business Goals Speed Quality Cost Culture Innovation SDLCTimeTakenWeeks Hours Low Test Automation Continuous Testing Autonomous Testing
- 14. Current UI testing challenges Proprietary & Confidential 14 • Unreliable Object Recognition • Test Framework Design • Inadequate Prioritization • Test refactoring • Test scalability • Inadequate Documentation TEST DESIGN TEST MAINTENANCE AND EXECUTION
- 15. How AI could address these challenges Proprietary & Confidential 15 INTELLIGENT TEST DESIGN INTELLIGENT TEST MAINTENANCE AND EXECUTION Advanced Object Recognition Framework Generation Responsive Web Design Risk Profiling Predictive Self-Healing Intelligent Bug Hunting Netflix Scryer and Simian Army Process Automation
- 16. What does Intelligent Test Design Look Like? Proprietary & Confidential 16 Risk Profiling Prioritize tests based on business risk by environment, configuration, and different aspects of your application Object Recognition Automatically recognize new objects and updates to add them to the DOM and structure without manual effort Framework Generation Automatically scan your application to recommend a test framework
- 17. Each application update and a new test case can act as inputs to help maintain an application. Proprietary & Confidential 17 Example Outputs: • Recommendation systems • Reinforcement learning • Device selection Example Inputs: • Application controls • Properties of controls • New test cases • New test results
- 18. What does Intelligent Test Execution and Maintenance Look Like? Proprietary & Confidential 18 Predictive Self-healing Dynamically update your test suite when your application changes or evolves Netflix Scryer and Simian Army Achieving resilience by applying predictive auto-scaling and continuous fitness functions. Intelligent Bug Hunting Discover bugs in the application through AI-powered exploratory testing Process Automation Automate business workflows for end- to-end testing
- 19. Proprietary & Confidential 19 Without AI: a lot of time spent on… Failures Refactor Tests Object Recognition Defects Found Repetition Stability Bottle Necks Decision Time Resource Allocation
- 20. Proprietary & Confidential 20 With AI: gives us more time to focus on… Creativity Scalability Customer Satisfaction Test Coverage Business Impact Results
- 21. Property-based and visual recognition together achieves the maximum level of test coverage Proprietary & Confidential 21 Visual Testing • Dynamic properties • Faster maintenance • Scalability Hybrid Recognition • Max. test coverage • Faster test design and maintenance Manual Testing • Exploratory benefits • High maintenance • Low accuracy • Low test coverage Property-Based • Accuracy / Stability • Distinguish similar objects EaseofMaintenance Level of Accuracy High Low Low High
- 22. • Slack Chatbot • Visual Objects Recognition by Shapes and Colors • Intelligent Code Completion • Intellectual Modern Documentation • Controlling the WebUI by using gestures Recent SmartBear Hackathon Proprietary & Confidential 22
- 23. Design Commit Test Deploy Monitor Manage UI API Collaborate on user stories, tests, and code Commit and push new code and tests to kick off CI Create, manage, and execute automated tests Deploy to production once tests and builds pass Monitor the performance of your APIs and web apps Analyze and improve all of your testing Reduce defects and improve trust with code review SwaggerHub Design, model, & share API definitions HipTest Design features & tests using BDD Zephyr Native Jira & enterprise test management Review Collaborator Code, document, & artifact review CrossBrowserTesting Run Selenium & Appium tests in the cloud LoadNinja Automated UI performance testing TestComplete Automated UI functional testing ServiceV Pro Virtualize APIs, UIs, and web services LoadUI Pro Automated API performance testing SoapUI Pro Automated API functional & security testing AlertSite Global, synthetic API monitoring
- 24. Thank You
Editor's Notes
- #17: Mention TestIO here as companies trying to address this complete Applitools Mabl Lino to expand risk profiling – scientist and engineer being on the same project
- #18: Funny for input output joke? Intelligent system is based on inputs and outputs Link to expand on what other inputs and outputs are involved in an AI model Each change to an application and a new test case can act as inputs to help update object definitions and descriptions. And each can change quickly as software developers enhance the application, adding new objects with new functionality or updating object definitions and descriptions while they are fixing bugs.
- #19: Mention TestIO here as companies trying to address this complete Applitools Mabl Lino to expand risk profiling – scientist and engineer being on the same project
- #22: Certainly, many companies have used AI to automate processes, but those that deploy it mainly to displace manual work will see only short-term productivity gains. In our research involving 1,500 companies, we found that firms achieve the most significant performance improvements when humans and machines work together. The same applies to today’s object recognition. Through hybrid recognition, humans and AI actively enhance each other’s complementary strengths: stability, and accuracy, of the former, and the speed and scalability of the latter to perfect recognition reliability. What comes simple to property-based recognition (identifying subtle differences between two images, for example) can be tricky for machines, and what’s straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans. Testing requires both kinds of capabilities.
- #26: Animated SmartBear logo