ICSE 2027 (International Conference on Software Engineering) is a CCF A / CORE A* / QUALIS A1 conference held in Dublin, Ireland on 2027-04-25. The paper submission deadline is 2026-06-23. Acceptance notifications are sent on 2026-10-20.
The International Conference on Software Engineering (ICSE) is the premier forum for presenting and discussing the most recent and significant technical research contributions in the field of Software Engineering. In the research track, we invite high-quality submissions of technical research papers describing original and unpublished results of software engineering research.
ICSE 2027 will follow a single deadline structure. In other words, submissions will occur in one cycle. Further details in the section on Single Submission Cycle below.
Research Areas
ICSE welcomes submissions addressing topics across the full spectrum of Software Engineering, namely quantitative, qualitative, and mixed-methods research. Topics of interest include the following, grouped into nine research areas. Please note that these topics are by no means exhaustive.
Each submission will need to indicate one of these nine areas as the chosen area. Optionally, the authors can consider adding an additional area. A paper may be moved from the chosen area(s) to another focus area at the discretion of the program chairs. Program chairs will ultimately assign a paper to an area chair, considering the authors’ selection, the paper’s content, and other factors such as (if applicable) possible conflicts of interest.
AI for Software Engineering
AI-enabled recommender systems for automated SE (e.g., code generation, program repair, AIOps, software composition analysis, etc.)
Human-centered AI for SE (e.g., how software engineers can synergistically work with AI agents)
Trustworthy AI for SE (e.g., how to provide guarantees, characterize limits, and prevent misuse of AI for SE)
Sustainable AI for SE (e.g., how to reduce energy footprint for greener AI for SE)
Collaborative AI for SE (e.g., how AI agents collaborate for automating SE)
Automating SE tasks with LLM and other foundation models (e.g., large vision model)
Efficacy measurement beyond traditional metrics (e.g., accuracy, BLEU, etc.)
Prompt engineering for SE (e.g., novel prompt design)
AI-assisted software design and model driven engineering (e.g., specification mining, program synthesis, software architectural design)
Analytics
Mining software repositories, including version control systems, issue tracking systems, software ecosystems, configurations, app stores, communication platforms, and novel software engineering data sources, to generate insights through various research methods
Software visualization
Data-driven user experience understanding and improvement
Data driven decision making in software engineering
Software metrics (and measurements)
Architecture and Design
Architecture and design measurement and assessment
Software design methodologies, principles, and strategies
Theory building for/of software design
Architecture quality attributes, such as security, privacy, performance, reliability
Modularity and reusability
Design and architecture modeling and analysis
Architecture recovery
Dependency and complexity analysis
Distributed architectures, such as microservice, SOA, cloud computing
Patterns and anti-patterns
Technical debt in design and architecture
Architecture refactoring
Adaptive architectures
Architecture knowledge management
Dependability and Security
Formal methods and model checking (excluding solutions focusing solely on hardware)
Reliability, availability, and safety
Resilience and antifragility
Confidentiality, integrity, privacy, and fairness
Performance
Design for dependability and security
Vulnerability detection to enhance software security
Dependability and security for embedded and cyber-physical systems
Evolution
Evolution and maintenance
API design and evolution
Release engineering and DevOps
Software reuse
Refactoring and program differencing
Program comprehension
Reverse engineering
Environments and software development tools
Traceability to understand evolution
Human and Social Aspects
Focusing on individuals (from program comprehension, workplace stress to job satisfaction and career progression)
Focusing on teams (e.g., collocated, distributed, global, virtual; communication and collaboration within a team), communities (e.g., open source, communities of practice) and companies (organization, economics)
Focusing on society (e.g., sustainability; diversity and inclusion)
Focusing on programming languages, environments, and tools supporting individuals, teams, communities, and companies.
Focusing on software development processes
Requirements and Modeling
Requirements engineering (incl. non-functional requirements)
Theoretical requirement foundations
Requirements and architecture
Feedback, user and requirements management
Requirements traceability and dependencies
Modeling and model-driven engineering
Variability and product lines
Systems and software traceability
Modeling languages, techniques, and tools
Empirical studies on the application of model-based engineering
Model-based monitoring and analysis
Software Engineering for AI
SE for AI models
SE for systems with AI components
SE for AI code, libraries, and datasets
Engineering autonomic systems and self-healing systems
Automated repair of AI models
Testing and verification of AI-based systems
Validation and user-based evaluation of AI-based systems
Requirements engineering for AI-based systems
Testing and Analysis
Software testing
Automated test generation techniques such as fuzzing, search-based approaches, and symbolic execution
Testing and analysis of non-functional properties
GUI testing
Mobile application testing
Program analysis
Program synthesis (e.g., constraint based techniques)
Program repair
Debugging and fault localization
Runtime analysis and/or error recovery
Scope
Since the authors will choose an area for their submission, the scope of each area becomes important. Some submissions may relate to multiple areas. In such cases, the authors should choose the area for which their paper brings the maximum new insights. Moreover, authors also have the choice of indicating an alternate area for each paper.
Similarly, for certain papers, authors may have a question whether it belongs to any area, or is simply out of scope. For such cases, we recommend that the authors judge whether their paper brings new insights for software engineering. As an example, a formal methods paper with a focus on hardware verification may be deemed out of scope for ICSE. In general, papers that only peripherally concern software engineering and do not give new insights from the software engineering perspective would be less relevant to ICSE. Our goal, however, is to be descriptive rather than prescriptive, enabling authors to make their own decisions about relevance.