Abstract. With digitalization and with technologies such as software, data, and artificial intelligence, companies in the embedded systems domain are experiencing a fundamental shift in how they develop, deploy, maintain and evolve their systems. Also, dimensions such as how to align with partners for innovation and new value creation, as well as models for how to monetize this value change dramatically when physical systems are increasingly being complemented with software services, data-driven offerings, and digital products. This talk explores the opportunities that come with continuous deployment of software and how companies in the embedded systems domain can benefit from data collection and feature experimentation practices in the transition from traditional towards digital companies. The talk focuses on the changes associated with processes and ways-of-working, but also new engagement models for value creation and monetization. To illustrate the transition, the talk provides concrete examples based on action-oriented research conducted in close collaboration with companies in the the embedded systems domain.

Bio. Helena Holmström Olsson is a professor of Computer Science at Malmö University, Sweden and a senior researcher in Software Center (software-center.se). Her research interests and expertise include engineering aspects of AI systems, data driven development practices, digitalization and digital transformation, and software and business ecosystems. She is the supervisor of several PhD students in the area of data driven development and AI engineering, focusing in particular on the business and organizational implications of AI deployment, and she has a well-established and continuous collaboration with the European software-intensive embedded systems industry.
Her research is published in high-quality software engineering journals and conferences and she is program chair for the “2nd International Conference on AI Engineering - Software Engineering for AI” (CAIN) 2023. Helena is a fellow member of the International Software Product Management Association (ISPMA) and a board member of Malmö University, Sweden.

Abstract. In this talk we will provide an overview of our recent work on diversity and inclusion in software engineering. We discuss both the aspects related to teamwork (e.g., community smells) and to experience of individual developers (e.g., ability to express themselves, ageism and sexism). While most of the talk focuses on gender as the diversity attribute, we also touch on issues related to culture and age. In the second part of the talk, we discuss methodological issues inherent in studying gender in the software engineering context.
This talk is based on a series of papers published in 2019-2023 and co-authored with Sebastian Baltes, Ann Barcomb, Sterre van Breukelen. Gemma Catolino, Filomena Ferrucci, Stefano Lambiase, Tiago Massoni, Nicole Novielli, Fabio Palomba, Camila Sarmento, and Damian Andrew Tamburri.

Bio. Alexander Serebrenik is a full professor of social software engineering at the Eindhoven University of Technology, The Netherlands. His research goal is to facilitate evolution of software by taking into account social aspects of software development. His work tends to involve theories and methods both from within computer science (e.g., methods from natural language processing, machine learning) and from outside of computer science (e.g., organizational psychology). He is actively involved in the organization of scientific conferences and is a member of the editorial board of several journals. He has won multiple best paper and distinguished reviewer awards.

Abstract. There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. In this keynote we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges.
There is an ICST 2023 keynote paper related to this tutorial. The tutorial talk will be given by Mark Harman. The paper is by Nadia Alshahwan, Mark Harman and Alexandru Marginean. Thanks to the many Meta engineers, managers and leadership for their assistance, support and work on deploying automated software testing and improvement techniques.

Bio. Mark Harman is a full-time Research Scientist at Meta Platforms in the Instagram Product Performance team, working on software, engineering automation. He was previously in the Simulation-Based Testing (SBT) team at Meta, which he co-founded. The SBT team developed and deployed both the Sapienz and WW platforms for client- and server- side testing. Sapienz grew out of Majicke (a start up Mark co-founded) that was acquired by Facebook (now Meta Platforms) in 2017. Prior to working at Meta Platforms, Mark was head of Software Engineering at UCL and director of its CREST centre, where he remains a part time professor. In his more purely scientific work, he co-founded the field Search Based Software Engineering (SBSE) in 2001. He received the IEEE Harlan Mills Award and the ACM Outstanding Research Award in 2019 for his work, and was awarded a fellowship of the Royal Academy of Engineering in 2020.

Abstract. Bots are applications that can react to various external stimuli, such as events triggered by tools or messages posted by users, and perform automated tasks in response. They often include conversational capabilities to interact with end-users through textual messages in the same communication channels as their human counterparts. Bots are widely used in various domains, including e-commerce, customer service, and education. Software development is no exception: bots can play a significant role in taming the complexity of software projects by automating repetitive tasks and communicating the outcomes back to stakeholders, designers, developers, and end-users. The recent advances in natural language processing and artificial intelligence— which led, for instance, to tools like ChatGPT and Bard— bring new opportunities and challenges to bot usage and development. In this talk, we will discuss the current role of bots in software engineering, best practices for (chat)bot development, and the challenges that lie ahead.

Bio. Mairieli Wessel is an assistant professor at Radboud University, The Netherlands. Upon receiving her Ph.D. in Computer Science from the University of São Paulo (USP), she was awarded the Best Brazilian Ph.D. Thesis on Software Engineering in 2021. Her research interests include software engineering (SE) and computer-supported cooperative work (CSCW). Recent projects include the development of tools and strategies to support open-source software communities and the design of bots and chatbots.

Abstract. While we all are convinced that Machine Learning can greatly help many different software engineering activities, from requirements elicitation to software testing and maintenance, there are probably some doubts that software engineering can help ameliorate machine learning-based systems. This talk wants to try to answer this question without asking chatGPT, but by presenting some first experiences carried out in our research group. The presentation will discuss some first attempts at healing machine learning models, at improving the deployment and operation of federated learning systems and at provisioning heterogeneous resources to ease and optimise the training and inference phases of machine learning-based systems. The final goal is to try to identify some recurring problems and some open issues for a plausible research agenda.

Bio. Luciano Baresi is full professor at the Politecnico di Milano - Dipartimento di Elettronica, Informazione e Bioingegneria, where he earned both his laurea (master) degree and PhD in computer science. Luciano was visiting professor at University of Oregon (USA) and visiting researcher at University of Paderborn (Germany). Luciano was program chair for ICECCS, FASE, ICWE, ICSOC, SEAMS, ESEC/FSE, and SCC, and general chair for WICSA/CompArch. He is currently the steering committee chair of ESEC/FSE. Luciano has co-authored diverse papers and a book in Italian. His research interests are in the broad area of software engineering. At the beginning he was interested in formal approaches for modeling and specification languages, he then moved to UML and the design of Web applications. Currently, he is interested in distributed systems, service-based applications, and in the different aspects of mobile, self-adaptive, and pervasive software systems.

Abstract. 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.

Bio. Massimiliano Di Penta is full professor at University of Sannio, Italy. Previously, he has been associate professor (2011-2019) and assistant professor (2004-2011) at the same University. He got his PhD in Computer Science Engineering in 2003, and his Master Degree in 1999. He is currently director of the bachelor and master program in computer engineering, and he's teaching digital design in the Bachelor Degree in Computer Science Engineering, and Software Engineering and Video Game Development in the Master Degree in Computer Science Engineering.