3 papers
cs.SE2026
Watts and Debts of Agentic Frameworks: An Empirical Study (Registered Report)
Aneetta Sara Shany, Chandrasekar S, Karthik Vaidhyanathan
Context: Every agentic AI system shipped to production carries two hidden risks: accumulated Technical Debt (TD) and unmonitored runtime energy costs. While functional benchmarking…
cs.SE2025
Approach Towards Semi-Automated Certification for Low Criticality ML-Enabled Airborne Applications
Chandrasekar Sridhar, Vyakhya Gupta, Prakhar Jain +1
As Machine Learning (ML) makes its way into aviation, ML enabled systems including low criticality systems require a reliable certification process to ensure safety and performance…
cs.SE2025
LoCoML: A Framework for Real-World ML Inference Pipelines
Kritin Maddireddy, Santhosh Kotekal Methukula, Chandrasekar Sridhar +1
The widespread adoption of machine learning (ML) has brought forth diverse models with varying architectures, and data requirements, introducing new challenges in integrating these…