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TechDebt 2027: International Conference on Technical Debt

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截稿日期:
2026-10-19 还有 13 天
通知日期:
2027-01-11
会议日期:
2027-04-25
会议地点:
Dublin, Ireland
届数:
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ICORE: B   浏览: 2994   关注: 0   参加: 0
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全站第 693 名 / 共 5,693 个会议 · 前 13%
学术认可 (35%)
72
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65
社区关注 (10%)
13
资料公开度 (15%)
55

用到的输入: 收录等级：ICORE B · 有据可查的届次：10 · 过去 24 个月打开过本页的研究者：5 人

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征稿

TechDebt 2027 (International Conference on Technical Debt) is a ICORE B conference held in Dublin, Ireland on 2027-04-25. The paper submission deadline is 2026-10-19. Acceptance notifications are sent on 2027-01-11.

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 discussing 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 10th International Conference on Technical Debt will be held on April 25th and 26th, 2027, in Dublin, Ireland. As the previous editions, TechDebt will be co-located with the 49th International Conference on Software Engineering (ICSE 2027). 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 Approaches for Managing 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
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