Technical Debt (TD) is a powerful metaphor for the trade-offs and compromises made during software development. While traditionally explored in well-established systems, the proliferation of data-driven and highly complex systems in recent years has opened up novel challenges and opportunities for studying and managing TD. This special issue invites contributions investigating how emerging concerns in AI, data-intensive systems, and other evolving domains affect our understanding and management of TD.
These emerging concerns often introduce new implications for Technical Debt research and practice, including:
Data Quality and Evolution: In data-driven and AI systems, poor data quality or rapidly evolving datasets can create significant "data debt," affecting system performance and maintainability.
Ethical and Fairness Considerations: In AI systems, addressing fairness, transparency, and bias creates new types of debt that must be managed to maintain system trustworthiness.
Scalability Challenges: Systems designed for rapid scalability often incur architectural debt that can hinder future growth and adaptation
In particular, the scope broadens beyond specific domains, such as IoT or AI-enabled systems, to encompass a wide range of "new" concerns. Examples include but are not limited to:
Data-driven systems that rely on machine learning, analytics, or real-time processing.
AI-enabled systems, including machine learning pipelines, autonomous decision-making platforms, and systems combining symbolic and statistical AI.
Cyber-physical systems, including but not limited to autonomous vehicles, robotics, and smart infrastructures.
Distributed and cloud-native architectures add complexity to TD through dynamic resource allocation, microservices, and evolving interfaces.
Emerging security and privacy concerns, particularly as they intersect with TD in sensitive systems.
We encourage submissions that clearly motivate and justify the "novelty" of the systems or concerns being studied, particularly in relation to existing TD literature. Contributors are expected to provide rigorous empirical studies, evidence-based analyses, or innovative methodologies that advance our understanding of TD in these emerging areas.
Topics of Interest
We seek submissions that address the following topics or related areas:
Manifestations of TD in data-driven, AI-enabled, cyber-physical, or distributed systems.
Novel methodologies to measure, analyze, or manage TD in these contexts.
Case studies showcasing real-world experiences with TD in modern systems.
Impact assessments of TD on performance, maintainability, scalability, or security.
Longitudinal studies tracking the evolution and lifecycle of TD in emerging domains.
Tool evaluations that address TD detection, management, or mitigation in innovative ways.
Studies on practitioner experiences with TD in AI and data-intensive systems.
Submissions must clearly articulate how the chosen domain or system introduces unique considerations or challenges for TD research and practice.