STAREAST 2019 - Big Data, Analytics, AI/Machine Learning for Testing
Monday, April 29
Testing Strategies for Microservices
Software 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...
Tuesday, April 30
Artificial Intelligence and Machine Learning Skills for the Testing World
PreviewSoftware 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...
Test Data: Mining, Morphing, Managing and Maintaining It!
NewAccording to the 2018/2019 World Quality Report, the number one challenge in applying testing to agile development is overcoming the challenges of creating, managing, and maintaining test environments and test data. Over 48% of respondents had issues with test data. As our systems complexity and time to market demands have increased, the appetite for resolving the test data issue can be diminished or be viewed as test data doesn’t really matter. Join Julie Gardiner as she shares the good, bad and ugly of test environments and data, defines an approach to establish where you are in terms of...
Wednesday, May 1
Data Curation: Refine and Shine
PreviewWe now live in a world where data is generated with every action taken. From buying groceries to walking the dog, we're generating data all the time, everywhere. Companies are starting to undertake harnessing that data efficiently for business cases, and that requires developing a process around data curation. This process must determine which data to curate, how to maintain curated data, and when to delete stale data. A robust data curation process agreed upon by stakeholders is essential to mining data effectively if you want to strike gold. Michael Hobbs will walk through the...
Big Data Migration to the Cloud: Testing Challenges and Strategies
PreviewMoving to the cloud is no longer a question of if, but when. Most corporations are either underway in their cloud adoption or have it on their radar. Typically the move from on-premise to cloud is a few hops and different types of data, such as SQL or some version of a file. Couple this with data transformations and it poses a challenge to testing and QA. How do you validate at each hop? Is it required to validate contents between source and destination? Can this testing be automated? Do we build a tool to automate these steps or purchase one? In this session, Sanjay Srinivas will...
The Dell EMC Journey in the Age of Smart Assistants
Dell EMC is driving to optimize and reimagine their testing practices with the application of data-driven smart assistants, powered by analytics and machine learning. At a macro level, Geoff Meyer will highlight the opportunities across the product engineering and testing landscapes that are ripe for the application of analytics and AI. Key ingredients in moving toward solutions that matter are the identification of organization-specific pain points, their prioritization, and the availability and cleanliness of essential data. Geoff will share the process of experimentation, staffing, and...
Future-Proofing Test Engineers in the Era of ML and AI
PreviewWe're all hearing the buzzwords of AI, machine learning, chatbots, and next-generation testing. Does this mean that the days of traditional testing as we know and practice it are over? Eran Kinsbruner doesn't think so. Join Eran to learn about the clear transformation happening toward smarter testing techniques and tools. These approaches drive better pipeline efficiency and release velocity with high quality, and Eran thinks this means good things for the testing practice and practitioners. Discover the key trends that are happening around AI, machine learning, and bots in the web...
Fishbowl Discussion: Continuous Testing
Thursday, May 2
Where Does Data Come From?
With all the tools available on the market, it can be overwhelming to determine which ones might meet your needs and which ones will work best in your environment to create a high-performing team. Join Jennifer Bonine as she explains the relationship of the DevOps cycle, your environment, and how a hub-and-spoke model can link all your different data sets and tools together. Jennifer will identify opportunities for applying test data analytics across the engineering and test landscape, ranging from high-value test cases to dynamically generated regression test suites. She will review ways...
Testing Large Data Sets with Supervised Machine Learning
Price rate is used to calculate an insurance premium based on the different insurance coverage. Every year the price rate is based on updated regulations, so after each change, the new price rate has to be tested for a large amount of data to make sure that the premium is correct based on the coverage. Testing fifty thousand data entries and their variations is impossible for any testing team. Alireza Razavi will present an AI automation testing framework designed to solve this testing problem. Discover how to use a supervised machine learning algorithm to determine the type of training...