Microservice logs analysis employing AI: A systematic literature review

Thu, 01 Jan 2026 00:00:00 +0000

GraphQL-Aware Healing in Service-Oriented Architectures via Multi-Signal Learning

Wed, 01 Jan 2025 00:00:00 +0000

AI-Driven Software Testing

Sat, 01 Jun 2024 00:00:00 +0000

Overview

Modern software systems — from virtual reality applications to microservice architectures — present unique testing challenges that traditional approaches struggle to address. This project explores how large language models and AI techniques can be applied to automate and improve software testing across these domains.

Research Directions

LLM-Based VR Testing: We investigate using LLMs for exploration testing of VR applications, where the state space is large and traditional test generation methods are insufficient. Our work demonstrates how LLMs can generate meaningful interaction sequences that achieve high code coverage.

Code Clone Detection in VR Software: Empirical studies on code cloning patterns specific to VR software development, identifying unique maintenance and security implications.

Software Security in VR: Investigating software security weaknesses across VR projects, examining when and why vulnerabilities emerge during the development lifecycle.

Microservice Log Analysis with AI: Systematic analysis of how AI techniques can be applied to microservice log data for anomaly detection, root cause analysis, and system health monitoring.

Microservices | Sen He

Microservice logs analysis employing AI: A systematic literature review

GraphQL-Aware Healing in Service-Oriented Architectures via Multi-Signal Learning

AI-Driven Software Testing

Overview

Research Directions