Speech Title:
Dependable Intelligence and Reasoning Beyond ChatGPT+
with an Application to Sports Analytics
Abstract: Machine Learning (ML) systems have become increasingly integral to safety and security-critical applications. However, a significant challenge arises from the inherent lack of explainability and verifiability in many ML systems. Our recent research has focused on addressing this issue by developing a Trusted ML system. The initial segment of this presentation delves into the "Silas: Trusted Machine Learning System," an initiative that seamlessly integrates open machine learning with formal automated reasoning (www.depintel.com). In the subsequent part of the discussion, we explore the reasoning capabilities of ChatGPT+ (encompassing ChatGPT3.5 and GPT4). Specifically, we discuss the approaches to link ChatGPT+ with formal reasoning techniques, aiming to establish a framework for trusted LLM agents. As a practical demonstration, we will present the application of probabilistic model checking, machine learning, LLM, and computer vision to sports analytics and share the vision of a new international sports analytics conference series (https://formal-analysis.com/isace/2024/).
Biography: Jin Song Dong is a professor at the National University of Singapore. His research interests include safety and security systems, sports analytics, and trusted machine learning/LLM reasoning. He co-founded the commercialized PAT verification system which has garnered thousands of registered users from over 150 countries. Jin Song co-founded the commercialized trusted machine learning system Silas. He has received numerous best paper awards and served on the editorial board of ACM Transactions on Software Engineering and Methodology and Formal Aspects of Computing. He has successfully supervised 30 PhD students and is an Institute of Engineers Australia Fellow. In his leisure time, Jin Song developed Markov Decision Process models for tennis analysis using PAT, assisting professional players with pre-match analysis (beating the world's best). He is a Junior Grand Slam coach and coached tennis to his three children, all of whom have reached the #1 national junior ranking in Singapore/Australia. Two of his children have earned NCAA Division 1 full scholarships. His second son, Chen, played #1 singles for Australia in the Junior Davis Cup Final and participated in both the Australian Open and US Open Junior Grand Slams.
Speech Title:
Reliability
Evaluation of Modular Software Systems with Bug
Prediction
Abstract: Software
bug prediction aims at predicting bug-prone modules
in advance during
the module testing, and is reduced to a statistical
discrimination problem, where several
kinds of machine learning algorithms are applied to
predict the bug-prone probability in each
module before conducting the module test. However,
it should be noted that the reliability
evaluation of modular software systems with bug
prediction has not been considered yet in
the past literature. In this paper, we focus on the
assumption that estimates of the bug-prone
probabilities are not mutually identical, and
develop a reliability evaluation method for the
modular software systems with bug prediction.
Throughout numerical illustrations
with actual software development project data, we
present how to utilize our reliability modeling
and inference in the testing of modular software
systems.
Biography: Dr. Tadashi Dohi has served as a Full Professor at Hiroshima University, Japan, since 2002. He is currently appointed as Dean of School of Informatics and Data Science and Associate Dean of Graduate School of Advanced Science and Engineering, Hiroshima University. He received a Doctor of Engineering degree from Hiroshima University in 1995. His research interests include Software Reliability, Dependable Computing, Performance Evaluation, Operations Research. To date, his research has led to 280 journal papers, 340 peer-reviewed conference papers, 25 book editions, and 47 book chapters in the above research fields. Dr. Dohi is a Regular Member of IEICE, IPSJ, REAJ, a Fellow Member of ORSJ, and a Senior Member of IEEE (Computer Society and Reliability Society). He was acting President of REAJ in 2018 and 2019. He has served as the General Chair of 15 international conferences, including ISSRE 2011, ATC 2012, DASC 2019, and ICECCS 2022. Of note, he was a founding member of the International Symposium on Advanced Reliability and Maintenance Modeling (APARM) and International Workshop on Software Aging and Rejuvenation (WoSAR). He has been a steering committee member in AIWARM/APARM, ISSRE, DASC, DSA. He has also worked as a program committee member in several premier international conferences such as DSN, ISSRE, COMPSAC, SRDS, QRS, EDCC, PRDC, HASE, SAC, ICPE, among numerous others. He is an Associate Editor/Editorial Board Member of over 20 international journals, including IEEE Transactions on Reliability.
Speech Title:
Development of System Software Stack for RISC-V+AI Computility
Abstract: In recent years, the RISC-V open ISA has gained much progress rapidly and has become a focus of international technology competition. It has also become an effective way to consolidate industrial development consensus through open source and build a global industrial computility ecosystem. Although Nvidia's GPUs and CUDA software ecosystem currently dominate the global AI computing market, the industry urgently hopes to establish a new software ecosystem to break through CUDA ecosystem barriers. A gradually formed consensus is to use RISC-V AI chips as a common ground, unite related companies and universities/research institutes to jointly develop ISA AI extension standards in an open source and open manner, and cooperate in the development of an open-source AI system software stack on top of these standards. This presentation discusses this direction, its significant opportunities, and the strategies for addressing the faced challenges.
Biography: Tao Xie is a Peking University Chair Professor, Chair of the Department of Software Science and Engineering in the School of Computer Science at Peking University, and Chief Scientist of Beijing Institute of Open Source Chip. He was a Full Professor at the Department of Computer Science, the University of Illinois at Urbana-Champaign (UIUC), USA. He is a Foreign Member of Academia Europaea, and a Fellow of ACM, IEEE, AAAS, and China Computer Federation (CCF). He won NSF Faculty CAREER Award, ACM SIGSOFT Influential Educator Award, ACM SIGSOFT Distinguished Service Award, IEEE TCSE Distinguished Service Award, MSR Foundational Contribution Award, ASE 2021 Most Influential Paper Award, etc. He serves as Director of CCF Technical Committee of System Software (TCSS), RISC-V+AI Computility Ecosystem (RACE) Committee Chair, RISC-V International AI/ML SIG Chair, and Co-Editor-in-Chief of Wiley Journal of Software Testing, Verification and Reliability (STVR).
Speech Title:
Innovating Software Engineering Education in the Era of Artificial Intelligence
Abstract: The
rapid advancement of artificial intelligence (AI)
and the widespread availability of online resources
are disrupting traditional approaches to software
engineering education. As students increasingly turn
to self-directed learning, the conventional
classroom model—focused on theoretical knowledge and
single-subject assessments—is losing its
effectiveness. Traditional software engineering
curricula, with their emphasis on theory and
standardized evaluations, are becoming increasingly
disconnected from the fast-evolving industry. With
AI's growing role in software engineering practices,
including automated code generation, testing, and
predictive maintenance, it is imperative to equip
future engineers with the skills demanded by this
changing landscape.
Despite the clear need for a transformation in
software engineering education, the rigid and
time-consuming processes involved in updating
curricula often prevent timely alignment with
technological advancements. Furthermore, the
continuous upgrading of equipment, technical
support, and faculty expertise is both costly and
resource-intensive. This necessitates a shift in
educational focus from simply imparting knowledge to
developing students’ ability to learn, adapt, and
excel in a rapidly changing environment.
This presentation advocates for a fundamental
rethinking of software engineering education,
emphasizing practical, interdisciplinary approaches
that better prepare students to navigate and thrive
in the evolving landscape of the field. We propose
integrating innovation- and work-based learning into
the curriculum through three models: community-based
projects, industry-partnered final year projects,
and multi-course joint assignments that foster
interdisciplinary collaboration. These models not
only provide students with valuable hands-on
experience but also cultivate resilience and
adaptability—critical traits for success in a
dynamic field.
While these models present significant benefits,
they also pose challenges, including resource
constraints, the difficulty of aligning academic
objectives with industry needs, and the requirement
for faculty who can bridge the gap between
traditional education and the demands of modern
software engineering. This presentation will explore
these challenges and propose solutions, arguing for
a reimagined educational framework that prioritizes
practical skills, interdisciplinary learning, and
the development of adaptive, resilient professionals
equipped to lead in an AI-driven world.
Biography: Dr. Thiam Kian CHIEW is a Professor at the Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur. He leads the Research and Innovation in Software Engineering (RISE) Research Group and previously served as Deputy Dean for Postgraduate Studies (2016–2019). His research focuses on usability and interoperability software systems, and implementation of e-health solutions. Dr. Chiew has been instrumental in developing innovative solutions for diabetes clinical registry, hospital queue management, COVID-19 patient home monitoring and national vaccination monitoring systems, virtual patients for medical education. He collaborates closely with academia, industry, and NGOs. Dr. Chiew also champions the integration of research, education, and innovation in software engineering, striving to transform tertiary education to address future challenges.