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