Spalazzese / Vaidhyanathan / Capuano | Software Architecture. ECSA 2026 Tracks and Workshops | Buch | 978-3-032-39142-1 | www.sack.de

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Lecture Notes in Computer Science

Spalazzese / Vaidhyanathan / Capuano

Software Architecture. ECSA 2026 Tracks and Workshops

Bolzano, Italy, September 7–11, 2026, Proceedings
Erscheinungsjahr 2026
ISBN: 978-3-032-39142-1
Verlag: Springer

Bolzano, Italy, September 7–11, 2026, Proceedings

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Lecture Notes in Computer Science

ISBN: 978-3-032-39142-1
Verlag: Springer


This book constitutes the refereed proceedings of the tracks and workshops which complemented the 20th European Conference on Software Architecture, ECSA 2026, held in Bolzano, Italy, during September 7–11, 2026.

The 15 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 43 submissions.

The book includes the proceedings of the following tracks and workshops co-located with ECSA 2026:

– ECSA/ESOCC Doctoral Symposium

– Tools and Demos Track

– 1st International Workshop on Architecting Secure, Intelligent, and Sovereign Agentic Systems
(ASISAS)

– 9th Context-Aware, Autonomous and Smart Architectures International Workshop (CASA@ECSA
2026)

– 1st International Workshop on Intelligent and Data-driven Engineering for Software Architecture
(IDEA-ARCH)

Spalazzese / Vaidhyanathan / Capuano Software Architecture. ECSA 2026 Tracks and Workshops jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


.- Doctoral Symposium .

.-  Architecting Self-Adaptive Systems with Learned and Symbolic 
Components.

.- Decentralised Dynamic Reconfiguration of Compositional Software 
Architecture.

.- Generative AI Support for Architecting Energy-Efficient Robotics 
Software.

.- Toward Reliable Agentic AI for Software Architectural Analysis.

.- Ensuring Safe and Repeatable Feature Activation for Energy Adaptation.

.- Sustainable Agentic AI: Mitigating Uncertainty-Driven Degradation.

.- Tools and Demos.

.- YAMAS: Yet Another Machine Learning Automation System.

.- K8sPCM: A Domain-Specific Language for Cloud-Native Performance 
Modeling Workflows.

.- Sarch: An Integrated Tool for Architecture-Based Security Assurance.

.- CASA@ECSA Workshop.

.- Software Architecture for Agentic AI: Challenges and Way Ahead.

.- Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 
Systems: An Agent-Based Multi-Level Approach to Hierarchical 
Structural Architecture Reconstruction.

.- Persona-as-Configuration: Generative Stakeholder Reporting for 
Agricultural Floods.

.- RunSoC 2.0: Scheduling and Allocating Automotive Software Tasks to 
Hardware Partitions in Heterogeneous MPSoCs.

.- IDEA-ARCH Workshop.

.- AI Assistance for Architectural Decision Making: Domain-Driven Context 
and Prompt Engineering.

.- Intelligent Detection of Design Patterns in Existing Source Code.

.- Bridging Metadata Gaps in Service APIs via Generative AI-Based 
Inference.

.- An Automated Context-Aware Approach for Architecturally Significant 
Requirements Identification from Natural Language Requirement 
Specifications.

.- From Requirements to Design Decisions: A Four-Stage Methodology 
Pipeline for ASR-Driven Architectural Tactic Recommendation Using 
Retrieval-Augmented Generation.

.- Can LLMs Extract Architectural Design Decisions from Source Code 
Commits? - A Preliminary Exploratory Study.

.- Analyzing the Adoption of LLMs to Support Software Architecture: A 
Systematic Mapping Study.

.- ASISAS Workshop. 

.- Investigating the Impact of API Design Techniques for Tools in Agentic 
Systems.

.- Decoupling Reasoning from Execution: A Deterministic Runtime 
Architecture for Safe Autonomous LLM Agents.

.- On the Trade-offs of LLM-Based Multi-Agent Architectures for 
Operations Research.

.- Where It Lives Is Not What It Is: An Architectural Vocabulary for 
Retained Adaptation in Agentic Systems.

.- Tool-based Data Segregation: a Prevention Strategy against Prompt 
Injection in AI Agents.

.- Mother Tree: A Methodology-First, Choreographed Multi-Agent System 
for Individual–Collective Commercial Intelligence.

.- Towards a Software Architecture Design Support Tool for Compliance 
with the EU AI Act.



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