Agile + DevOps East 2023 - AI/ML
Monday, November 6
A Quality Engineering Introduction to AI and Machine Learning
Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...
Leveraging Generative AI for Software Productivity
Executive leaders across the globe have been asking a relatively simple yet profound question: Can we leverage generative AI to transform our business, enterprise, or industry? For software-based companies, focus has either been on differentiating their product and service offerings using this new technology. But how about leveraging generative AI to improve team productivity and efficiency? This may be possible but how do you measure its success? What are some of the key use cases within business analysis, development, and testing that software teams can use? Are there any pitfalls...
Tuesday, November 7
Supercharge Your Workflow: To GitHub and Beyond
Whether you are new or experienced with GitHub this class is for you! Supercharging your workflow caters to anyone who wants to enhance their Agile and DevOps process with the capabilities of GitHub. GitHub has long been the premier site for open-source projects and is now turning a pivotal corner into becoming the predominant platform for all aspects of the development lifecycle. Some examples of this include; protecting company code through various GitHub Products or curating marketplace actions and workflows prior to use. This tutorial will look at how to leverage GitHub Actions (CI/CD...
Getting Started with AI and Machine Learning
Are you a software professional who would like to learn to use AI and machine learning (ML), but don't know how to get started? One of the best ways to get into ML is by designing and completing small projects. Although you will ultimately need to understand the fundamentals of AI/ML, there's no reason why you can't learn foundational terms, concepts and principles as you put them into practice. Join Dionny Santiago as he introduces you to the world of applied machine learning. Dionny will guide you through a series of ML projects end-to-end, enabling you to gain experience with creating...
Wednesday, November 8
Integrating Generative AI in Your Workflow
Delve into the vital importance of Generative AI and its profound impact across various domains, revolutionizing workflows and fostering innovation and creativity. As the digital landscape rapidly evolves, harnessing generative AI becomes imperative to remain at the forefront of progress. Attendees will explore how this cutting-edge technology unlocks the potential to generate realistic and novel content, spanning from images to music and text, fundamentally transforming traditional workflows. Three key takeaways are emphasized: First, the integration of generative AI empowers attendees to...
Exploring Non-ChatGPT Ways to Use AI in Testing
Artificial intelligence and machine learning tools like ChatGPT are bringing AI to the public. The goal and purpose of code generation and refactoring tools like GitHub’s Co-Pilot are evident. However, it may not be as obvious to tell which tools are specifically tailored for testing and quality engineering. Join Carlos Kidman as he compares multiple AI/ML tools and practical applications that testers and quality engineers can use right now. You’ll discover AI for functional testing solutions and tools for test data management. While not all of these tools are free, this session will give...
Addressing Security Risks In LLM-Based Applications
Large Language Models continue to grow in popularity as people experiment, applying them to problems and pushing new code into production applications. Growing along with this popularity is an engineering approach that advocates outsourcing more and more of an application’s functionality to these LLMs. But what seems like an advantage on the surface masks different costs and risks. Ultimately, you may end up with less reliable code that’s harder to troubleshoot and fix, accruing technical debt along the way. There’s also the potential increase in attack surface from integrating LLMs into...
AI in DevOps for Improving Engineering Team Productivity
AI and machine learning continue to be hot topics everywhere. The majority of discussions on AI/ML are focused on how generative AI and large language models (LLMs) will change the world. However, LLMs like ChatGPT, Bard, and Llama only represent one area of modern advances in the AI/ML space. Considering the bigger picture of new AI technologies, Chris Navrides has been investigating ways to enable entire new areas of opportunity within the DevOps lifecycle. By leveraging AI in DevOps, he believes teams can improve developer productivity through better code authoring, debugging, and...
AI-Powered Agile + DevOps: The Future Starts Now!
Generative Artificial Intelligence (GenAI) is disrupting industries worldwide, everything from automotive to manufacturing, finance, healthcare, and more. Interestingly, GenAI is transforming the very process used to create the software systems it embodies. Just like in video games, AI is giving the software development lifecycle (SDLC) some much needed power-ups in the form of increased speed and enhanced capabilities. But are these power-ups true force multipliers or simply the essentials for progressing to the next stage? Are they temporary or permanent? Join Tariq King as he travels...
Copilot Wants to Write Code and Be Your Friend!
Have you ever wanted to be able to pair program by yourself? Did your coworker write incoherent code at 3 in the morning? Do you hate writing unit tests by hand? If you answered yes to any of these, then Copilot is the perfect tool for you! Copilot will suggest code snippets as you work based on the assumptions it makes on what you are trying to accomplish. It can explain those pesky chunks of code that you don’t understand, saving you valuable time. You can even have Copilot write out unit test code for you based on the methods that you are trying to test. Merging two tabs into one, dive...
From Bottlenecks to Breakthroughs: Using AI in Performance
Performance, a cornerstone of delivering high-quality software, is poised to undergo a seismic shift, and artificial intelligence (AI) is at the helm. Yet, as with many technological evolutions, there's a haze of misconception surrounding AI's role in this domain. Is AI merely an advanced tool, or can it redefine the entire engineering paradigm? Join Kaushal Dalvi as he demystifies AI's transformative role in performance. Drawing from real-world experiences and case studies, covering AI's potential in auto-generation of performance tests and predicting bottlenecks. Engage in interactive...
Thursday, November 9
Chasing Predictability with AI: The Model of You Outperforms You
Usually, the first question a client asks about software development is: "When will it be done?". Traditional methods to answering this question are fraught with errors. The most common errors include heavy reliance on estimates and the use of averages to give a deterministic answer. What goes through your mind when you try to answer this question? Now imagine that we could take the same process that your mind goes through and model it? Get rid of the biases that we as humans have by using the data our systems already track. Advances in AI, combined with the rapid growth of data across...
Generative AI in Quality Assurance
The need for a new test automation model has been an imperative over the last 10 years as we have moved from waterfall to agile and agile to DevOps. Moving from test coverage to application coverage and reducing test time from months to an hour or less has created a substantial pressure for full success. Now AI in test is a reality. The first generative AI offerings in QA became available in 2018 and since then marked improvements have been made in outcomes. This has changed QA teams' focus, tasks, and work effort. With the ultimate goal of AI finding all our bugs, the advent of...
Transforming User Requirements to Test Cases Using Model-Driven Software Engineering and Natural Language Processing
Testing continues to be the main approach to ensuring software quality during development. Although there have been many attempts to automate the generation of test cases from user requirements (formal or informal), creating test cases continues to be mainly a manual process. However, many studies have shown that automating the generation of test cases from requirements can substantially reduce costs and improve the efficiency of the testing process. Test automation has also been proven to show positive effects on software quality. With the advances in Model-driven Software Engineering (...
AutoOps: Harnessing the Power of AI-Augmented Testing with Generative AI
In the rapidly evolving landscape of software development, Continuous Integration and Continuous Deployment (CI/CD) have emerged as pivotal methodologies for delivering high-quality software at an accelerated pace. To ensure the reliability and robustness of software releases in such an environment, the marriage of cutting-edge technologies becomes imperative. Incorporating Generative AI into business process automation seemed daunting just a few years ago. However, the availability and popularization of OpenAI/ChatGPT and other AI/ML technologies have made it a closer reality, and now it...
Transforming Testing with Generative AI: A Demonstration
Are you looking to improve the efficiency and effectiveness of the agile testing process? Join and explore the future of agile testing with Generative AI. In agile software development, feature grooming, refinement, user story generation, acceptance testing, and test automation are all critical steps to ensure system quality. While these steps are critical to the project's success, they are not always handled effectively. They can be time-consuming and things like writing test cases and automation code can be labor-intensive and often fall short of covering all requirements. To address...