Key research themes
1. How can evolution support mechanisms optimize software product line process management?
This research theme investigates mechanisms to support incremental and complex evolution in software product line (SPL) processes. It emphasizes the importance of managing dependency relations among different artifacts (requirements, design, implementation) and defining meta-processes that enable both static and dynamic process migration. Given the ever-evolving nature of software products and their development environments, supporting evolution at the process level ensures consistency, quality, and adaptability of SPLs over time.
2. What role does variability management and tool support play in enhancing software product line engineering?
Variability management is key to handling commonality and diversity within software product families. This theme explores how defining variability through models (feature models, decision models) and managing their relationships (e.g., inclusivity, exclusivity) facilitate reuse and customization. Tooling is identified as a pivotal enabler for scalability, complexity handling, and industrial adoption of variability management approaches in SPL engineering.
3. What are effective strategies for integrating product and configuration management in complex engineering domains?
This theme addresses the integration of requirements and configuration management to maintain traceability and coherence among parts, subsystems, and systems throughout their lifecycle. Emphasis is placed on designing customizable, open-source data models that enable tracking of requirement origins and establishing unified approaches that avoid costly proprietary tools. The integration supports multidisciplinary collaboration, improves change management, and facilitates continuous airworthiness in highly regulated industries like aeronautical engineering.
4. How does generative AI transform the role of product managers and optimize product lifecycle activities?
Rapid advances in generative AI, including large language models and domain-specific AI tools, are reshaping product management practices across ideation, planning, design, development, and launch stages. This theme explores how AI augments human creativity, enhances cross-functional collaboration, streamlines workflows, and accelerates decision-making. The responsible adoption of AI tools fosters efficiency gains, strategic alignment, and personalized learning experiences for product managers, ultimately reshaping organizational product development capabilities.
5. What challenges and solutions exist in integrating AI into legacy inventory management systems in traditional industries?
Legacy inventory systems, often characterized by inflexible infrastructures and outdated technologies, pose significant barriers to AI integration in traditional industries. This theme explores the technical incompatibilities, organizational resistance, financial constraints, and expertise gaps that impede AI adoption. The research emphasizes strategic approaches and case study insights that enable successful AI integration to improve demand forecasting, real-time tracking, and inventory optimization while addressing legacy system challenges.