We are interested in the following topics

• Testing AI systems and agent architectures

  • Testing AI agents as software systems: functional, non-functional, and behavioral checks.
  • AI systems security.
  • Testing agent robustness and reliability.
  • Performance evaluation of AI components.
• GenAI for testing and QA processes

  • Generation of test cases and test data using LLMs.
  • Real-world effectiveness of AI in testing: how to measure value (ROI, coverage, defect leakage), not just demo results.
  • Limits of GenAI applicability in QA: which types of tasks are easily automated, and which are poorly suited or risky.
  • Use of AI in niche domains.
• Test automation and tools

  • New frameworks, libraries, plugins, and scenario engines for automated testing.
  • Test generation, TestOps, and support of test data.
  • Coverage metrics, mutation testing, fuzzing, quality analytics.
  • Integration of automation with DevOps and infrastructure (containers, cloud, build pipelines).
  • Reducing effort while maintaining high testing quality.
• Load and performance testing

  • Methods and tools for performance testing.
  • Traffic modeling, profiling, and bottleneck identification.
  • Measuring latency, throughput, time-to-response, SLA, and SLO.
  • Performance testing of UI, API, databases, and microservices.
  • Automation of performance tests, integration with observability tools.
  • Building and interpreting performance metrics.
• Mobile testing

  • UI testing, frameworks, UX on mobile platforms.
  • Performance: startup speed, FPS, power consumption, memory leaks.
  • Cross-platform solutions: Flutter, Kotlin Multiplatform, React Native, and their testing specifics.
  • CI/CD for mobile apps, build management, cloud devices, and emulators.
• Web applications and browser systems

  • Testing PWAs, WebView, and browser extensions.
  • Frameworks, DevTools.
  • Frontend performance and optimization: loading, rendering, caching.
  • Web application security: XSS, CSRF, GraphQL testing.
  • Web test automation, CI integration, headless browsers.
  • Deep testing of browser engines, WebAssembly, and low-level scenarios.
• Backend and server systems

  • Testing microservices, event-driven, and reactive architectures.
  • Load and integration testing of APIs, RPC, and message-broker systems.
  • Observability: metrics, tracing, logging, alerting.
  • Infrastructure and fault-tolerance testing.
  • Data quality and integration scenarios between systems.
• Hardware, desktop, and embedded systems

  • Testing embedded firmware, IoT devices, software-hardware interaction.
  • Desktop applications: UI, performance, cross-platform support (Windows, macOS, Linux).
  • Emulators, simulators, physical test benches, and automation tools.
  • Testing security and resilience of hardware solutions.
• Testing infrastructure

  • Test benches, environments, and data: local, cloud, hybrid.
  • Use of mocks, stubs, synthetic data, and sandbox infrastructure.
  • CI/CD for testing: orchestration, environment isolation, reproducibility.
  • Containerization, infrastructure as code.
  • Monitoring test bench stability and dependency management.
  • Effective organization of testing infrastructure in scalable teams.