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X-ORIGINAL-URL:https://lassonde.yorku.ca/
X-WR-CALNAME:Lassonde School of Engineering
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DTSTART;TZID=America/Toronto:20240726T093000
DTEND;TZID=America/Toronto:20240726T103000
DTSTAMP:20240726T144700
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CREATED:20240726
LAST-MODIFIED:20240726
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:AI + SE Seminar Series: Chunyang Chen
DESCRIPTION:The incredible advances in AI/ML (Artificial Intelligence/Machine Learning) over the last few years are enabling the development of AI-enabled software systems. The trustworthiness and quality assurance of such systems remains a difficult challenge. At the same time, foundational models like Large Language Models (LLMs) show that various software development tasks (e.g., coding) can now be supported with AI-based suggestions (e.g., intelligent code completions)\nThe AI+SE seminar series invites speakers from academia and industry who are working at the intersection of AI and SE (SE = Software Engineering), such as the design of AI-enabled systems, quality assurance and testing of AI/ML models and their operationalization into software systems, as well as the modernization of traditional software development practices and works with ML-based techniques.\nLearn more about the AI + SE Seminar Series. ( https://lassonde.yorku.ca/eecs/eecs-upcoming-events/ )\nJuly 26:Mobile Application Testing with Large Language Models: Landscape and Vision\nMobile apps are now indispensable for people’s daily life. To ensure the app quality, automated GUI testing is widely explored for locating bugs. However, there are many issues with current GUI testing tools including low activity coverage, excessive overhead, and missing issues of app usability (e.g., GUI aesthetics or animation) and accessibility (e.g., to the aged and disabled like the blind). The emergence of powerful Large Language Models (LLM) brings an opportunity to overcome these GUI testing issues. In this talk, he is going to introduce his latest works on different aspects of mobile app testing such as boosting GUI testing coverage, testing case generation, and automated visual bug replay, by leveraging LLM methods including GPT-3/4, and ChatGPT. In addition to the academic publications mentioned above, he will also share a landscape of existing works in using LLM in software testing and share the potential future directions in this field.\nBio\nDr Chunyang Chen is a full professor in the School of Computation, Information and Technology, Technical University of Munich, Germany. His main research interest lies in automated software engineering, especially data-driven mobile app development. Besides, he is also interested in Human-Computer Interaction and software security. He has published 100+ research papers in top venues such as ICSE, FSE, ASE, CHI, CSCW with extensive collaboration with industry, including Google, Microsoft, and Meta. His research has won awards including ACM SIGSOFT Early Career Researcher Award, Facebook Research Award, four ACM SIGSOFT Distinguished Paper Awards (ICSE’23/21/20, ASE’18), and multiple best paper/demo awards.\nRegister on Zoom ( https://yorku.zoom.us/meeting/register/tJ0td-quqT0oGdUYjzBQTNdeU8axI6KSFFsS )\n \n
URL:https://lassonde.yorku.ca/events/ai-se-seminar-series-3-238
ORGANIZER;CN=Lassonde School of Engineering:MAILTO:
CATEGORIES:Electrical Engineering & Computer Science
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