Lu Zhang is a professor at the School of Computer Science, Peking University, P.R. China. He received both PhD and BSc in Computer Science from Peking University in 2000 and 1995 respectively. He was a postdoctoral researcher in Oxford Brookes University and University of Liverpool, UK. He served on the program committees of many prestigious conferences, such FSE, OOPSLA, ISSTA, and ASE. He was a program co-chair of SCAM2008 and a program co-chair of ICSME17. He was on the editorial boards of Journal of Software Maintenance and Evolution: Research and Practice and Software Testing, Verification and Reliability. His current research interests include software testing and analysis, program comprehension, software maintenance and evolution, and software reuse.

Source code is essential in software evolution and reengineering. In this talk, I argue that deep neural code models are of great importance when applying deep learning for various evolution and reengineering tasks for software. Following this argument, I will discuss two categories of deep neural code models. Deep neural code models in the first category are models trained with neural networks specifically designed for code. These models can consider some important syntactic and semantic characteristics of code in the first place. Deep neural code models in the second category are models pre-trained with source code using neural networks designed for pre-training with natural language corpora. These models can take the advantage of big code. Furthermore, I will analyze some evolution and reengineering tasks that deep neural code models can achieve state-of-art results in comparison with traditional techniques. Finally, I will provide some of my thoughts on the next-step research on deep neural code models.

Massimiliano Di Penta is a full professor at the University of Sannio, Italy. His research interests include software maintenance and evolution, mining software repositories, empirical software engineering, search-based software engineering, and software testing. He is an author of over 300 papers that appeared in international journals, conferences, and workshops. He has received several awards for research and service, including four ACM SIGSOFT Distinguished paper awards. He serves and has served in the organizing and program committees of more than 100 conferences, including ICSE, FSE, ASE, and ICSME. Among others, he has been program co-chair of ASE 2017, ESEC/FSE 2021, and will be program co-chair of ICSE 2023. He is co-editor in chief of the Journal of Software: Evolution and Processes edited by Wiley, editorial board member of ACM Transactions on Software Engineering and Methodology and Empirical Software Engineering Journal edited by Springer, and has served the editorial board of the IEEE Transactions on Software Engineering. Home Page: https://mdipenta.github.io

During the last decade, we have observed flourishing research leveraging artificial intelligence techniques to tackle several software engineering problems. This is not much different from what it can be observed in other engineering and science disciplines. The reasons are manyfold and include (i) fast research advances in artificial intelligence, (ii) suitable hardware and software support, and, last but not least, (iii) growing availability of data. As software engineering researchers, what would be our role in this rapidly changing world? Should we wait for better models from artificial intelligence researchers, apply them (almost) out of the box, obtain better results, and be happy with that? Are big companies already developing tools able to answer any question a developer may ever ask? Like doctors in medicine, which combine the use of artificial intelligence with their profound domain knowledge and make a parsimonious usage of that, our role should not be one of passive AI users. Rather, we are responsible, in different forms, for making such techniques applicable, effective, and efficient in software engineering development. This not only means leveraging our domain knowledge to create better and better AI-based software engineering tools but also to figure out in what circumstances these tools can effectively help developers, as well as how the developers' role is expected to change in this rapidly mutating scenario.

Helen D. Karatza (senior member of IEEE, ACM, SCS) is a Professor Emeritus in the Department of Informatics at the Aristotle University of Thessaloniki, Greece. Dr. Karatza's research interests include cloud, fog and mist computing, energy efficiency, fault tolerance, resource allocation, scheduling algorithms and real-time distributed systems. Dr. Karatza has authored or co-authored over 240 technical papers and book chapters including seven papers that earned best paper awards at international conferences. She served as an elected member of the Board of Directors at Large of the Society for Modeling and Simulation International. She served as chair and keynote speaker in international conferences. Dr. Karatza is Senior Associate Editor of the Elsevier journal “Simulation Modelling Practice and Theory”, an Editor of “Future Generation Computer Systems” of Elsevier, and an Associate Editor of IEEE Transactions on Services Computing. She was Editor-in-Chief of the Elsevier journal “Simulation Modelling Practice and Theory”, Editor-in-Chief of “Simulation Transactions of The Society for Modeling and Simulation International”, Associate Editor of “ACM Transactions on Modeling and Computer Simulation” and Senior Associate Editor of the “Journal of Systems and Software” of Elsevier. She served as Guest Editor of Special Issues in several international journals. More info about her activities and publications can be found at: https://users.auth.gr/karatza/.

Cloud computing has been successfully adopted as a computing model. However, due to the recent growth of the Internet of Things (IoT) and the development of smart environments, traditional Cloud computing is insufficient in transferring very large amounts of data generated by IoT devices. As a result, Fog and Mist computing have appeared as computing paradigms to provide lower transmission latency. Typically, the IoT applications are time-critical and the primary concern is meeting deadlines. Therefore, appropriate scheduling of real-time applications is required to increase the efficiency of cloud, fog and mist computing architectures. In this talk we will present potential research directions driving the development of new computing paradigms. Furthermore, novel techniques to explore challenges in scheduling of real-time applications in cloud, fog and mist computing environments will be presented and discussed.

Nachiappan Nagappan is currently a Research Scientist at Meta Platforms. He has published over 100 journal and conference publications of which thirteen have received distinguished, test-of-time or best paper awards. He is an ACM Fellow, IEEE Fellow and received the 2020 IEEE-CS Harlan D. Mills award. He is on the editorial board of Springer’s journal Empirical Software Engineering, is an Associate Editor of both the IEEE Transactions on Software Engineering and IEEE Transactions on Reliability. He has served as program co-chair for the two main research conferences in his research area, the Empirical software engineering and measurement conference (ESEM) and the International Symposium on Software Reliability (ISSRE).

In this talk I will highlight challenges facing the code review process and its implications on productivity. This talk features results from both academia and industry. I will first highlight challenges facing modern code review, including finding the right reviewer with expertise to code review response times and factors that affect it. Then I will highlight opportunities via interventions both in the tool and the process and will then present results of real deployments and their implications. I will conclude with a future outlook on modern code review.

Zhendong Su is a Professor in the Department of Computer Science at ETH Zurich. He had previously been a full professor in Computer Science and a Chancellor's Fellow at UC Davis. He is a Member of the Academia Europaea, and a Fellow of the ACM and of the IEEE. He served on the steering committees of ISSTA and ESEC/FSE, served as an Associate Editor for ACM TOSEM, co-chaired SAS 2009, program chaired ISSTA 2012, and program co-chaired SIGSOFT FSE 2016. His research spans programming languages and compilers, software engineering, computer security, deep learning, and education technologies.

The key mission of computer science is to help people construct reliable, performant, and usable software. To this end, there have been substantial conceptual, technological, and practical advances for engineering high-quality software---we have better processes, languages, compilers, and development tools. On the other hand, the fundamental processes and toolchains are not significantly different from those in the early days of the field. In this talk, I will share some reflections on how we may advance the science and practice of engineering software. The talk highlights several areas and directions that I believe are critical and offer promising opportunities for significantly moving our field forward.

Tsong Yueh Chen received his BSc and MPhil from The University of Hong Kong, MSc and DIC from the Imperial College London, and PhD from The University of Melbourne. He is a Professor of Software Engineering at Swinburne University of Technology, Australia. Prior to joining Swinburne, he taught at The University of Hong Kong and The University of Melbourne. Chen is the inventor of adaptive random testing and metamorphic testing, and co-coined the term of failure-based testing.

Metamorphic testing has been extensively used in the discipline of software testing as a testing method to generate test cases or to address the oracle problem. However, it has merged to be applicable outside the discipline of software testing. In this talk, we would like to present some potential research directions for MT and their challenges outside the discipline of software testing. We could like to cover discussions on how to use MT to systematically train deep neural networks, how to do fault localisation, how to do program repair, how to extend the slicing family, etc.