This book constitutes the proceedings of the 34th International Conference on Software and Data Engineering, SEDE 2025, held in New Orleans, LA, USA, during October 20 21, 2025.

The 26 full papers presented in these proceedings were carefully reviewed and selected from 42 submissions. These papers focus on a wide range of topics within Software and Data Engineering and are categorized into the following topical sections: Software Engineering and Data Science & Artificial Intelligence.

Inhalt

.- Software Engineering and Data Science.

.- Drone Simulation in Precision Agriculture Using Unity.
.- Leveraging Generative AI for Proactive Security and Automated Remediation in Cloud-Native CI/CD Pipelines.
.- Optimizing Healthcare Pipelines for patient Benefit: A Data Engineering Perspectives on Preau-thorization Delays and Denials.
.- Pairwise Clustering on Numerical Datasets by Translation.
.- A Customizable Ad-hoc Java Client that Works with Bare Webservers.
.- DuckDB-Powered Geo-Spatial Analytics for hit-and-run Incidents: A Case Study on Montgom-ery County, Maryland, Open Data.
.- Analysis of Programming Capability of LLMs in the Context of Computer Science.
.- Predicting Early Breast Cancer Recurrence with Machine Learning.
.- Structural and Connectivity Patterns in the Maven Central Software Dependency Network.
.- Cloud-Native Generative AI for Automated Planogram Synthesis: A Diffusion Model Approach for Multi-Store Retail Optimization.
.- Applications Of Positive Unlabeled Learning in the field of DDoS attacks.
.- Robust Intrusion Detection in IoV Using PU Learning and Supervised Ensembles with Synthetic Data Augmentation on CICIoV2024.
.- The Potential of Large Language Models in Automating Software Testing: From Generation to Reporting.
.- Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings.
.- Hybrid Taint Analysis for React: Automated XSS Prevention.

.- Artificial Intelligence.

.- Edge-Based Learning for Improved Classification Under Adversarial Noise.

.- Prompt-Driven Test Generation: Leveraging Large Language Models and Knowledge Graphs for Quality Assurance in Data-Intensive Software System.

.- Adversarial Machine Learning for Robust Password Strength Estimation.

.- Mitigating Hallucination Risks in GenAI Compliance Advisory Systems for the Financial Industry.

.- Prosense - Defending Text Generation with Adversarial Feedbac.

.- Machine Learning-Based AES Key Recovery via Side-Channel Analysis on the ASCAD Dataset.

.- Hand Line Classification.

.- Beyond Accuracy: Evaluating LLMs for validating community service provider directory.

.- Trustworthy Design Patterns for Multi-Agent Software Systems.

.- Designing Interpretable AI Models: Lightweight Parallelism for Real-Time Malware Detection & Prevention.

.- Interpretable AI with Lightweight Parallelism for Real-Time Auto Insurance Claims Triage.