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A PASS session intro with <\/strong>Hamish Watson<\/a> and <\/strong>Saskia Parks<\/a><\/p>\n PASS Data Community Summit is coming around quickly, and I imagine, like us, you\u2019re all checking out the schedule and highlighting those sessions you\u2019re most interested in. Also like us, you might feel slightly inundated by the sheer number of sessions at events these days covering AI.<\/p>\n If you\u2019re feeling overwhelmed by it all, join us, DevOps alchemist, Hamish Watson<\/a>, and test data management-aficionado, Saskia Parks<\/a>, to chat through some real-world examples, demonstrating how machine learning (ML) and AI can help remove bottlenecks and inefficiencies, while enabling you to deliver better quality updates you can trust.<\/p>\n Here’s a taster of what we\u2019ll be covering.<\/p>\n There are many organizations using test data that doesn\u2019t truly represent production. They might not have enough test data to execute meaningful testing against, or they\u2019re using an old, shared environment that\u2019s been continuously added to and hasn\u2019t been refreshed in years. Perhaps production data isn\u2019t even an option due to all the sensitive customer information it contains. In these cases, dev and test might light up green, but when you go to release to production, things break.<\/p>\n How does AI help? Data generation is a brilliant option for creating the exact test data you need, whether that\u2019s using a small data set and generating more or creating data from scratch. However, this can miss edge-cases or neglect the nuance in the shape and diversity of your data. When you couple data generation with machine learning, you can generate synthetic, realistic test data that mimics real-world conditions, while entirely safeguarding sensitive customer information and ensuring compliance with regulations like GDPR and HIPAA.<\/p>\n We\u2019ll demonstrate how one of the greatest advantages of AI in testing and development is its ability to identify failures early in the development cycle by helping with the creation of both the tests and the test data. This approach supports the shift-left testing strategy \u2013 moving testing earlier in the development process to find issues earlier in the process where they\u2019re easier and cheaper to fix. The result? Fewer bugs, more stable releases, and quicker deployments.<\/p>\n We\u2019ll also cover what this looks like in CI\/CD pipelines, where manual testing and the unavailability of quality test data on demand causes bottlenecks. We\u2019ll talk through how AI steps in to automate tasks, including identifying potential failures early, and continuously adapting to evolving code changes, allowing you to accelerate time to market while improving software quality.<\/p>\n AI\u2019s ability to synthetically generate diverse test data enables you to cover a broad range of scenarios, including edge cases. These rare, difficult-to-reproduce scenarios are often missed in manual testing. What\u2019s more, it\u2019s not just the test cases that machine learning can help with, AI synthetic data generation can spin up the exact data required for each test. This gives you the exact data you need, when you need it, to ensure your work is thoroughly tested, without the worry of sensitive information in those lower environments.<\/p>\n In addition to improving test coverage, AI can help eliminate biases within test data. Whether intentional or unintentional, biased data can lead to discriminatory outcomes in software, such as unfair credit scoring algorithms or biased hiring platforms. Machine learning tools can analyze your test datasets for potential biases and help balance them, ensuring that your software delivers fair and inclusive results and serves all users fairly.<\/p>\n We all know AI and machine learning can be daunting, and you may feel like your processes have to change a lot to start leveraging them. However, we\u2019ll cover tips and tricks for getting started without having to completely overhaul your workflows and delaying work.<\/p>\n We\u2019ll cover how to make the most of AI so you can find the right approach for you. This includes taking it a step at a time, starting with clearly defined goals, and focusing on what you want to achieve first. Are you looking to improve test data availability and quality? Or perhaps automate tests and catch security vulnerabilities early?<\/p>\n AI isn\u2019t just a buzzword anymore. By integrating it into your processes, whether via DIY options or a third-party solution, you can future-proof your testing process, reduce bottlenecks and wait times, and deliver high-quality software faster than ever before.<\/p>\nEnhancing test data quality, security, and delivery<\/h2>\n
Early detection of failures and vulnerabilities<\/h2>\n
Comprehensive and inclusive test coverage<\/h2>\n
Best practices for implementing AI for testing and test data management<\/h2>\n
Want to know more?<\/h2>\n