STARWEST 2018 - Big Data, Analytics, AI/Machine Learning for Testing
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Monday, October 1
Artificial Intelligence and Machine Learning Skills for the Testing World
Software continues to revolutionize the world, impacting nearly every aspect of our work, family, and personal life. Artificial intelligence (AI) and machine learning (ML) are playing key roles in this revolution through improvements in search results, recommendations, forecasts, and other predictions. AI and ML technologies are being used in platforms for digital assistants, home entertainment, medical diagnosis, customer support, and autonomous vehicles. Testing practitioners are recognizing the potential for advances in AI and ML to be leveraged for automated testing—an area that still...
Tuesday, October 2
Testing Strategies for Microservices
NewSoftware development is trending toward building systems using small, autonomous, independently deployable services called microservices. Leveraging microservices makes it easier to add and modify system behavior with minimal or no service interruption. Because they facilitate releasing software early, frequently, and continuously, microservices are especially popular in DevOps. But how do microservices affect software testing and testability? Are there new testing challenges that arise from this paradigm? Or are these simply old challenges disguised as new ones? Join Tariq King as he...
Data Analytics and Machine Learning
Do you have access to lots and lots of test, development, app, and service data—really big data—from client and cloud service log files, test execution results, and more? Then, you have a great opportunity to begin using data analytics and machine learning (ML) to gain new product quality insights. Bring your laptops and your sense of discovery as Eun Chang introduce analysis techniques and ML tools to help you develop new and potentially groundbreaking insights. First, she will present a fast-paced statistics primer for those with no prior data exploration experience and others looking...
Wednesday, October 3
Fighting Test Flakiness: A Disease that Artificial Intelligence Will Cure
Artificial Intelligence (AI) is making it possible for computers to diagnose some medical diseases more accurately than doctors. Such systems analyze millions of patient records, recognize underlying data patterns, and generalize them for diagnosing previously unseen patients. A key challenge is determining whether a patient's symptoms and history are attributed to a known disease or other factors. Software testers face a similar problem when triaging automation failures. They investigate questions like, Is the failure due to a defect, environmental issue, or nondeterministic test script?...
Reduce Wait Time with Simulation + Test Data Management
Data has become the most significant roadblock that testers face today. In fact, up to 60% of a tester’s time is spent waiting for data. Chris Colosimo shows that many factors contribute to this wait time, including internal requirements from the test data management team to pull data in the proper form, wait times for sanitized or “test-safe” data, or, most importantly, building data sets that do not exist. Compounding these challenges is the inherit complexity of today’s data. You have to be a DBA to even begin to understand the structure and relationships needed to support your testing...
Marrying Artificial Intelligence with Software Testing: Challenges & Opportunities
Emerging technologies such as the internet of things (IoT) and cloud computing have introduced a significant software variety and complexity. Wendy Siew Wen Chin and Heng Kar Lau explain that testers are challenged to support a wide product portfolio within harsh time, resource and budget constraints. More test automation may seem to be a solution to test efficiency, however there are many inefficient hot spots throughout the test automation life cycle. Join Wendy and Heng Kar as they share their experiences from the Intel IoT team. They share how to make use of artificial intelligence (AI...
How to Automate Testing for Next-Generation Interfaces (BOTs, Alexa, Mobile)
PreviewToday’s IT systems communicate with customers through multiple points of engagement and various interfaces, ranging from web, mobile, and voice to BOTs and apps like Alexa and Siri. Sanil Pillai says these systems need to provide seamless handoffs between different points of interaction—while at the same time providing relevant and contextual information quickly. To accomplish this, a team must be able to successfully pair device hardware capabilities and intelligent software technologies such as location intelligence, biometric sensing, and Bluetooth. Sanil shows that testing...