Keynote 3: Prof. Abhik Roychoudhury
Title: AutoCodeRover: from research on agentic AI-based Programming to Spinoff Acquisition
Abstract: Automatic Programming, as the name suggests, achieves automation in software development. It is currently a topic of high technical and societal relevance, owing to the rise of generative AI. While disruptive innovations promising automated coding exist today, automatically generated code often carries with it a lesser degree of trust. The core challenge often comes a lack of understanding and extraction of developer intent, and using this intent in the automated coding. In this talk, we will first explore how symbolic program analysis methods can help extract developer intent from incomplete specifications such as a test-suite. These techniques SemFix and Angelix, allowed us to achieve automated program repair, where we rectify errors or vulnerabilities in code at scale. We will share past experiences in fixing vulnerabilities based on a single available test or exploit. We then discuss more recent works, where the correctness criterion guiding the repair is not provided by tests, but rather a bug report in natural language. We will discuss our Large Language Model agents AutoCodeRover and SpecRover, and discuss how the overall research goal of code intent extraction has influenced the design of these agents. AutoCodeRover was a spinoff from NUS which has been acquired by SonarSource in February 2025, with the goal of fixing vulnerabilities found by static analysis. We will conclude the talk with a discussion on how agentic AI may be shifting the balance in programming with trust in coding and code security becoming more important than programming at scale.
Bio: Abhik Roychoudhury is Provost’s Chair Professor of Computer Science at the National University of Singapore (NUS), where he leads a research team on Trustworthy and Secure Software (TSS). He is also Senior Scientific Advisor at SonarSource, subsequent to the acquisition of his spinoff AutoCodeRover on AI-based coding. Abhik received his PhD in Computer Science from the Stony Brook University in 2000, and has been a faculty member at NUS School of Computing since 2001. His research group at NUS is known for foundational contributions to software testing and analysis. Specifically the team has made contributions to automatic programming and automated program repair, as well as to fuzz testing for finding security vulnerabilities in software systems. These works have been honored with various awards including an International Conference on Software Engineering (ICSE) Most Influential Paper Award (Test-of-time award) for program repair, IEEE New Directions Award 2022 (jointly with Cristian Cadar) for contributions to symbolic execution (for test generation and program repair). Abhik was the inaugural recipient of the NUS Outstanding Graduate Mentor Award 2024. Doctoral students graduated from his research team have taken up faculty positions in many academic institutions including Max Planck Institute, NUS, University College London, University of Melbourne, Peking University and Concordia University. He has served the software engineering research community in various capacities including as chair of the major conferences of the field, ICSE and FSE. Currently, he serves as chair of the FSE steering committee. He is a member of the editorial board of Communications of the ACM. He is the current Editor-in-Chief of the ACM Transactions on Software Engineering and Methodology (TOSEM). Abhik is a Fellow of the ACM.
Keynote 4: Prof. Schahram Dustdar
Title: Active Inference for Distributed Intelligence in the Computing Continuum Image
Abstract: Modern distributed systems also deal with uncertain scenarios, where environments, infrastructures, and applications are widely diverse. In the scope of IoT-Edge-Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments. A captivating set of hypotheses from the field of neuroscience suggests that human and animal brain mechanisms result from a few powerful principles. If proved to be accurate, these assumptions could open a deep understanding of the way humans and animals manage to cope with the unpredictability of events and imagination. In this talk, we will explore how Active Inference mechanisms can be utilized for Distributed Intelligence in the Computing Continuum.
Bio: Schahram Dustdar is a Full Professor of Computer Science at the TU Wien, heading the Research Division of Distributed Systems, Austria and part-time ICREA research Professor at UPF Barcelona. He holds several honorary positions: University of California (USC) Los Angeles; Monash University in Melbourne, Shanghai University, Macquarie University in Sydney, and University Pompeu Fabra, Barcelona, Spain. From Dec 2016 until Jan 2017 he was a Visiting Professor at the University of Sevilla, Spain and from January until June 2017 he was a Visiting Professor at UC Berkeley, USA. From 1999 – 2007, he worked as the co-founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by ProjectNetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. He is the co-founder and chief scientist of Coovally.ai, an AI software infrastructure company based in Barcelona.He serves as Editor-in-Chief of Computing (Springer). Dustdar is the recipient of multiple awards: IEEE TCSVC Outs.
Future of Internetware Workshop Keynote 2: Prof. David Lo
Title: Efficient and Green Code LLMs: Happier Software Engineers, Happier Planet
Abstract: Many have been excited about the potential of code Large Language Models (code LLMs). However, code LLMs are large, slow, and energy-hungry compared to traditional ASE solutions, which raises usability and sustainability concerns. This is especially true when we want to deploy them in IDEs on local devices, which is often the preferred setting. This talk will highlight several strategies to improve the efficiency and energy consumption of code LLMs. It will also present a vision of what the future can be with efficient and green LLM and a call for action for more research in this direction to make both software engineers and our planet happier.
Bio: David Lo is the OUB Chair Professor of Computer Science and the Founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. Championing the area of AI for Software Engineering (AI4SE) since the mid-2000s, he has demonstrated how AI - encompassing data mining, machine learning, information retrieval, natural language processing, and search-based algorithms - can transform software engineering data into automation and insights. His contributions have led to over 20 awards - including two Test-of-Time awards and twelve ACM SIGSOFT / IEEE TCSE Distinguished Paper awards - and gathered close to 40k citations. An ACM Fellow, IEEE Fellow, ASE Fellow, National Research Foundation Investigator (Senior Fellow), and a recipient of the MSR Foundational Contribution Award and IEEE TCSE Distinguished Service Award, Lo has also served as a PC Co-Chair for ASE’20, FSE’24, and ICSE’25. For more information, please visit: http://www.mysmu.edu/faculty/davidlo/.
Future of Internetware Workshop Keynote 3: Prof. Miryung Kim
Title: Reinventing Testing for Big Data and Heterogeneous Computing
Abstract: The rise of big data, machine learning, and AI necessitates re-evaluating automated software testing techniques to achieve desired developer productivity gains. In this talk, I will reflect on my group’s experience of designing custom fuzzers for data-intensive computing and heterogeneous hardware domains. I will discuss the need to encode domain-specific constraints, custom feedback guidance, custom search strategies, and custom mutation operators to make the fuzzing solutions effective for a specialized domain. Then, reflecting on this manual specialization effort, I will discuss a new direction on how we should strive to bootstrap a domain-specific testing engine with minimal manual effort. Toward this vision of bootstrapping a domain-specific testing engine without paying too much, I will share several ongoing effort to find the right balance between the universality of a fuzzer and its effectiveness in a specialized domain: (1) custom mutation synthesis from examples, (2) automated grammar refinement to constrain fuzzing, (3) LLM-guided constraint-generation for mutation, and (4) a lightweight DSL for context-guided input generation.
Bio: Miryung Kim is a Professor and Vice Chair of Graduate Studies in Computer Science at UCLA. Her research group focuses on software engineering for AI, big data, and hardware heterogeneity. She has mentored seven PhD students and postdocs who have gone on to become professors (at Columbia, Purdue, and two at Virginia Tech, among others). For her impact on nurturing the next generation of academics, she received the ACM SIGSOFT Influential Educator Award. She served as Program Co-Chair of the ACM International Conference on Foundations of Software Engineering (FSE 2022). She was a Keynote Speaker at ASE 2019 and ISSTA 2022 and has given Distinguished Lectures at CMU, UIUC, and other institutions. She is an Amazon Scholar at Amazon Web Services.
Future of Internetware Workshop Keynote 4: Prof. Earl Barr
Title: Combatting Software Sprawl with LLMs
Abstract: Especially in manufacturing, many development tasks involve translation—converting natural language (often regulations) into formal engineering models, or bridging gaps between formalisms used by engineers with different expertise. While these translations are conceptually simple, they have historically been too costly to automate, forcing humans to handle tedious, repetitive work. A software process sprawls when it becomes clogged with such translation tasks, creating inefficiencies akin to urban sprawl’s fragmented, low-density development. Large Language Models (LLMs) now offer a solution: they excel at low-cost, largely accurate translation, effectively acting as skeleton keys against software sprawl. By reshaping the economics of translation, LLMs unlock new levels of automation, accelerating process velocity and freeing engineers to focus on creative, high-value work. In this talk, I define software sprawl and demonstrate how it stems from communication that blends formal and explanatory/regulatory channels. I introduce the Dual Channel Hypothesis to explain the interaction of these channels and show how LLMs can "polymerize” sprawl automating its consolidation and streamlining development.
Bio: Earl Barr is a professor of software engineering at the University College London. He received his PhD at University California Davis. Earl’s research interests include artificial intelligence for software engineering (and vice versa), debugging, testing and analysis, game theory, and computer security. His recent work focuses on probabilistically quantifying program equivalence, probabilistic type inference, and dual channel constraints. With the exception of a pandemic-imposed hiatus, Earl dodges vans and taxis on his bike commute in London.
Future of Internetware Workshop Keynote 7: Prof. Shaukat Ali
Title: Testing Cyber-Physical Systems with AI Foundation Models
Abstract: AI foundation models, trained on vast datasets spanning multiple modalities—such as text, audio, video, and images—are increasingly being integrated into various aspects of our daily lives. Their role across diverse sectors is expected to grow significantly in the years ahead. To this end, this talk will present how we used these models to test cyber-physical systems. The talk will present the methodologies employed, key results, and potential future applications. Moreover, the talk will discuss cases where these models fell short in supporting testing software systems, highlighting the challenges, limitations, and opportunities.
Bio: Shaukat Ali is the Head of Department, Research Professor, and Chief Research Scientist at Simula Research Laboratory, Oslo, Norway. His research focuses on devising novel methods for developing cyber-physical systems by applying various advanced techniques, such as artificial intelligence, digital twins, and quantum computing. He has led many national and European projects related to cyber-physical systems testing, search-based software engineering, model-based system engineering, and quantum software engineering.