DAIDE: Data and AI Driven Engineering

Motivation

Over the last decade, artificial intelligence (AI) and specifically machine- and deep-learning (ML/DL) solutions have become critical for software engineering. Because of the big data era, and with companies collecting customer and product data from an increasing number of connected devices, more data is available than ever before and can be used for analytics, for A/B testing, as well as for training ML/DL models. In parallel, the progress in high-performance parallel hardware, such as GPUs, TPUs and FPGAs, allows for training solutions of scales unfathomable even a decade ago. These two concurrent technology developments are at the heart of the rapid adoption of data-driven practices and ML/DL solutions in industry.

In industry, the hype around AI has resulted in virtually every company having some form of AI initiative, or host of AI initiatives, and the number of experiments and prototypes is phenomenal. However, research shows that the transition from prototype to industry-strength, production-quality deployment of ML/DL models proves to be challenging for many companies. The engineering challenges, and the related data management challenges, prove to be significant even if many data scientists and companies fail to recognize these.

One of the important application domains for AI is software engineering. AI can be used for, among others, generating code, test cases, documentation and regulatory compliance artefacts, software analytics, quality assurance, software processes, software visualization, human-computer interaction, adaptive systems, and data management., to mention a few. AI enables us to take the next step in excelling in software engineering and operations by delivering smarter software monitoring, analytics, development and management techniques, methods, and tools. Specifically, DevOps, DataOps and ML/AIOps, due to the periodic nature and significant amounts of data generated, are highly suitable for the application of ML/DL models.

This track is concerned with novel research results in engineering to facilitate production-quality AI solutions in all relevant domains, including software engineering.

Topics

Topics of interest include, but are not restricted to:

  • Solutions to assess and guarantee data quality for ML, including data pipelines
  • Design methods, approaches and tools for developing ML/DL models
  • Distributed ML/DL models in embedded systems, including federated learning and distributed AI
  • Methods, tools, applications and lessons learned of AI for software engineering
  • Automated labeling of data for ML
  • Adoption of DevOps, DataOps, and/or MLOps practices in large-scale software engineering
  • AI and analytics for DevOps
  • Engineering aspects of training, transfer learning and reinforcement learning
  • Engineering effective ML/DL deployments
  • Automated experimentation and autonomously improving systems
  • Feature experimentation and data driven development practices (e.g., A/B testing)
  • Reinforcement learning and multi-armed bandits
  • Explainable and compliant AI in the context of DevOps
  • Predictive methods and estimation in software development, operations, and evolution
  • Software visualization and visual analytics
  • System and software requirements and their relationship to AI/ML modeling
  • System and software architectures for AI-enabled systems
  • Software development life cycle for data and AI-enabled systems
  • Managing non-functional properties of data and AI-enabled systems
  • Empirical studies and experience reports about successful or unsuccessful applications of the aforementioned topics

We encourage full paper and short paper submissions demonstrating the benefits and/or challenges with regards to the development, deployment and evolution of the technologies mentioned above – as well as the adoption and application of the practices, tools and techniques related to these. We welcome submissions providing empirical case study data to illustrate how companies approach this shift in development paradigms.

Human – AI collaboration research papers

Human–AI collaboration in software engineering research examines how humans and AI systems jointly discover problems, generate hypotheses, collect and curate data, run studies, analyze evidence, and build theory and tools. Rather than replacing researchers, modern AI—LLMs, code intelligence, agents, and program analyzers—acts as a mixed-initiative partner: drafting study protocols, synthesizing prior work, mining repositories, generating and mutating code and tests, and surfacing anomalies that merit human inquiry. Researchers, in turn, provide problem framing, methodological rigor, domain judgment, and ethical oversight. Productive collaboration hinges on an explicit division of cognitive labor, traceable workflows (data provenance, prompt/version control), and evaluation loops that let humans verify, steer, and refine AI outputs. The goal is higher research throughput and quality: faster replication packages, richer multi-modal datasets, more comprehensive literature syntheses, and empirically grounded ins

This area also confronts hard constraints: construct validity when AI generates stimuli or labels; bias and leakage in training data; reproducibility of stochastic pipelines; intellectual property and security risks when using private code bases; and the need to measure collaboration quality, not just task accuracy. Promising directions include benchmarks for human-in-the-loop SE tasks, methods for uncertainty and reliability calibration, protocols for agentic experimentation at scale (with guardrails), and socio-technical studies of trust, workload, and skill evolution in research teams augmented by AI. Ultimately, human–AI collaboration aims to make SE research more cumulative, auditable, and impactful—speeding the path from question to evidence to actionable guidance for industry and open-source communities.

This new paper category invites contributions that explore the future of human–AI collaboration in software engineering research, including empirical studies, frameworks, methods, and tools that enhance or reimagine this interaction. We invite for full papers that address the end-to-end fundamental changes we see happening in software engineering research and the implications on industry. Papers need to consider the Springer guidelines on using AI. Also, we generally discourage systematic literature reviews as there is a separate track for this. Finally, we encourage authors to explicitly describe how the collaboration between humans and AI was structured as part of the research method section.

Track Organizers

  • Helena Holmström Olsson, helena.holmstrom.olsson@mau.se, Malmö University, Sweden
  • Jan Bosch, jan.bosch@chalmers.se, Chalmers University of Technology, Sweden & Eindhoven University of Technology, Netherlands

Program Committee

  • Matteo Camilli, Politecnico di Milano
  • Michel Chaudron, Eindhoven University of Technology
  • Anas Dakkak, Ericsson
  • Maya Daneva, University of Twente
  • Gabriele De Vito, University of Salerno
  • Rimma Dzusupova, McDermott
  • Christoph Elsner, Siemens AG
  • Aleksander Fabijan, Microsoft
  • Matthias Galster, University of Canterbury
  • Philipp Haindl, St. Pölten University of Applied Sciences
  • Petra Heck, Fontys
  • Jens Heidrich, Fraunhofer
  • Hans-Martin Heyn, University of Gothenburg
  • Jennifer Horkoff, University of Gothenburg and Chalmers University of Technology
  • Joran Leest, Vrije Universiteit Amsterdam
  • Torvald Mårtensson, Saab AB
  • Andreas Metzger, Paluno, University of Duisburg-Essen
  • Henry Muccini, University of L’Aquila
  • Rudolf Ramler, SCCH
  • Rodrigo Santos, UNIRIO
  • Daniel Ståhl, Ericsson AB
  • Matthias Tichy, Ulm University
  • Xiaofeng Wang, Free University of Bozen-Bolzano
  • Dietmar Winkler, Center for Digital Production (CDP) & TU Wien