ICSA – 2025

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ICSA – 2025

Dr. Karthik Vaidhyanathan and students  participated, presented papers and were volunteers at the 22nd IEEE International Conference on Software Architecture  (ICSA) 2025 held at Odense Denmark from 31 March to 4 April:

  • Dr. Karthik Vaidhyanathan’s undergraduate students Shrikara A and Meghana Tedla presented a research paper on LLMs for Generation of Architectural Components: An Exploratory Empirical Study in the Serverless World in the main track. Here is the summary of the paper as explained by the authors:

Recently, the exponential growth in capability and pervasiveness of Large Language Models (LLMs) has led to significant work done in the field of code generation. However, this generation has been limited to code snippets. Going one step further, our desideratum is to automatically generate architectural components. This would not only speed up development time, but would also enable us to eventually completely skip the development phase, moving directly from design decisions to deployment. To this end, we conduct an exploratory study on the capability of LLMs to generate architectural components for Functions as a Service (FaaS), commonly known as serverless functions. The small size of their architectural components make this architectural style amenable for generation using current LLMs compared to other styles like monoliths and microservices. We perform the study by systematically selecting open source serverless repositories, masking a serverless function and utilizing state of the art LLMs provided with varying levels of context information about the overall system to generate the masked function. We evaluate correctness through existing tests present in the repositories and use metrics from the Software Engineering (SE) and Natural Language Processing (NLP) domains to evaluate code quality and the degree of similarity between human and LLM generated code respectively. Along with our findings, we also present a discussion on the path forward for using GenAI in architectural component generation.

  • Dr. Karthik Vaidhyanathan’s Ph.D student Akhila Mattathammal presented her paper on EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model Switching on Resource-Constrained Edge Devices at the International Workshop on Flexible Software Architecture for Embedded Computing Systems (SARECS), co-located with  ICSA-2025. This research work was done in collaboration with Kriti Gupta, Larissa Lavanya, Ananya Vishal Halgatti, Priyanshi Gupta   and Karthik Vaidhyanathan. Here is the summary of the paper as explained by the authors:

The widespread adoption of machine learning on edge devices, such as mobile phones, laptops, IoT devices, etc., has enabled real-time AI applications in resource-constrained environments. Existing solutions for managing computational resources often focus narrowly on accuracy or energy efficiency, failing to adapt dynamically to varying workloads. Furthermore, the existing system lack robust mechanisms to adaptively balance CPU utilization, leading to inefficiencies in resource-constrained scenarios like real-time traffic monitoring. To address these limitations, we propose a self-adaptive approach that optimizes CPU utilization and resource management on edge devices. Our approach, EdgeMLBalancer balances between models through dynamic switching, guided by real-time CPU usage monitoring across processor cores. Tested on real-time traffic data, the approach adapts object detection models based on CPU usage, ensuring efficient resource utilization. The approach leverages epsilon-greedy strategy which promotes fairness and prevents resource starvation, maintaining system robustness. The results of our evaluation demonstrate significant improvements by balancing computational efficiency and accuracy, highlighting the approach’s ability to adapt seamlessly to varying workloads. This work lays the groundwork for further advancements in self-adaptation for resource-constrained environments. 

  • Dr. Karthik Vaidhyanathan presented his work on Leveraging LLMs for Dynamic IoT Systems Generation through Mixed-Initiative Interaction at the 1st International Workshop on Software Architecture and Generative AI (SAGAI) 2025, co-located  ICSA-2025.  This research work was done in collaboration with Bassam Adnan, Sathvika Miryala, Aneesh Sambu, Karthik Vaidhyanathan, Martina De Sanctis and Romina Spalazzese. Here is the summary of the paper as explained by the authors:

     Recently, the exponential growth in capability and pervasiveness of Large Language Models (LLMs) has led to significant work done in the field of code generation. However, this generation has been limited to code snippets. Going one step further, our desideratum is to automatically generate architectural components. This would not only speed up development time, but would also enable us to eventually completely skip the development phase, moving directly from design decisions to deployment. To this end, we conduct an exploratory study on the capability of LLMs to generate architectural components for Functions as a Service (FaaS), commonly known as serverless functions. The small size of their architectural components make this architectural style amenable for generation using current LLMs compared to other styles like monoliths and microservices. We perform the study by systematically selecting open source serverless repositories, masking a serverless function and utilizing state of the art LLMs provided with varying levels of context information about the overall system to generate the masked function. We evaluate correctness through existing tests present in the repositories and use metrics from the Software Engineering (SE) and Natural Language Processing (NLP) domains to evaluate code quality and the degree of similarity between human and LLM generated code respectively. Along with our findings, we also present a discussion on the path forward for using GenAI in architectural component generation.
  • Dr. Karthik Vaidhyanathan presented his work on Architecting Digital Twins for Intelligent Transportation Systems at the 4th International Workshop on Architecting and Engineering Digital Twins (AEDT) 2025, co-located  ICSA – 2025. This research work was  done in collaboration with Hiya Bhatt, Sahil, Karthik Vaidhyanathan, Rahul Biju, Deepak Gangadharan, Ramona Trestian, Purav Shah.  Here is the summary of the paper as explained by the authors:
    Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency.

  •  Dr. Karthik Vaidhyanathan, facilitated/co-chaired a working session on Generative AI for Software Architects. The working session was attended by around 40-50 academics and practitioners resulting in engaging discussions and takeaways.

 

April 2025

    
    
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