TechDebt 2026 (International Conference on Technical Debt) is an academic conference held in Rio de Janeiro, Brazil on 2026-04-12. The paper submission deadline is 2025-10-16. Acceptance notifications are sent on 2026-01-05.
Motivation
Technical Debt stands as a pivotal metaphor in the realm of software evolution, representing development shortcuts taken for expediency that cause the degradation of internal software quality. A critical element of the technical debt metaphor is its ability to bridge the communication gap between technical and non-technical stakeholders within software development teams. The International Conference on Technical Debt (TechDebt) is the flagship conference dedicated to discuss how to identify, address, and manage technical debt in software projects. This premier event unites leading researchers and practitioners in software engineering to explore diverse strategies for managing various forms of technical debt, share experiences and best practices, and identify the most pressing challenges faced by both industry and academia.
The 9th International Conference on Technical Debt will be held on Apri 12-13, 2026, in Rio de Janeiro, Brazil. As the previous editions, TechDebt will be co-located with the 48th International Conference on Software Engineering (ICSE 2026).
Topics
The TechDebt conference warmly invites research and practical contributions to its Technical Track. The topics of interest are organized around three main themes: Technical Debt Core, Software Modernization, and AI applied to Software Maintenance and Evolution. They include, but are not limited to:
Technical Debt Core
Investigations on Specific Technical Debt Types
Specific technical debt types (e.g., test debt, build debt, architectural debt)
Less studied kinds of technical debt (e.g., requirement, documentation, security debt)
Other types of debt (e.g., social debt, process debt)
Debt in specific domains (e.g., AI-based systems, mobile applications)
Case Studies and Practical Experiences
Case studies on successful and unsuccessful technical debt management practices
Case studies on the remediation of technical debt in real-world projects
Experiences from industry on managing and paying down technical debt
Empirical evidence on the effectiveness of technical debt management tools and approaches
Tools and Approaches for Managing Technical Debt
Tools, demos, and libraries to identify, assess, and manage technical debt
Methods and frameworks for identifying, monitoring, and managing technical debt
Decision frameworks for prioritizing debt items against features and other debts
Estimation of technical debt principal and interest
Quality assurance practices to minimize and address technical debt
Human and Organizational Factors
Human factors in managing technical debt (e.g., team dynamics, communication challenges)
Stakeholder perspectives and concerns about technical debt
The impact of organizational culture and processes on technical debt accumulation and repayment
Emerging Trends in Technical Debt Research
Use of artificial intelligence and machine learning for technical debt management
Software visualization techniques for technical debt identification and monitoring
New trends in technical debt for AI-driven systems and mobile applications
The role of software economics in shaping technical debt decisions
Position and Vision Papers
Position and vision papers offering novel perspectives on technical debt
New conceptual frameworks and metrics to study technical debt and its evolution
Software Modernization
Strategies and patterns for software modernization
Architecture recovery and reengineering
Legacy system analysis and transformation
Automated and semi-automated code refactoring
Reverse engineering and program comprehension
Model-driven approaches to modernization
Modernization of monolithic systems to microservices
Continuous modernization and evolution
Technical debt in modernization initiatives
DevOps and CI/CD implications on modernization
Empirical studies on modernization efforts
Tool support for software modernization
Measuring modernization progress and impact
Risks and challenges in modernization projects
Socio-technical aspects of system modernization
Case studies and industrial reports on modernization
AI applied to Software Maintenance and Evolution
AI and machine learning for code/design/architecture technical debt prediction and localization
Large Language Models (LLMs) for code understanding
AI-assisted code review and refactoring
Automated technical debt detection and remediation using AI
AI-based tools for software comprehension and documentation
AI-driven support for program analysis
Intelligent recommendation systems for software developers
Chatbots and virtual assistants for software maintenance tasks
Generative AI for software evolution and transformation
AI models for predicting software quality and maintenance effort
Evaluation frameworks for AI-based maintenance tools
Empirical studies on the effectiveness of AI in software maintenance
Human-AI collaboration in software evolution tasks
Threats and limitations of AI-based software maintenance/evolution tools