Call for papers is open

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

  • Generating test cases, test data, and fixtures using LLMs.
  • Real-world effectiveness of AI in testing: how to measure value, not just demo results.
  • Limits of GenAI applicability in QA: which types of tasks are easily automated, and which are poorly suited or risky.
  • AI coding tools in a QA engineer’s workflow: best practices, anti-patterns, AI-generated code review, and working without access to foreign services.
• Test automation and tools

  • New frameworks, libraries, plugins, and scenario engines for automated testing.
  • TestOps, test data management, and coverage metrics.
  • Property-based testing in production, fuzzing, and quality analytics.
  • Integration of test automation with DevOps pipelines, containers, and cloud environments.
  • Playwright Agents, MCP servers, and rebuilding UI test automation around them.
• Testing infrastructure

  • Test benches, environments, and data: local, cloud, hybrid.
  • CI/CD for testing: orchestration, environment isolation, reproducibility.
  • Containerization, infrastructure as code.
  • Monitoring test bench stability and dependency management.
  • FinOps for QA: the cost of CI test runs and LLM-as-a-Judge eval runs, and the economics of long-running end-to-end tests.
• 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.
  • Testing without Google services: on HMS, domestic firmware, and via MDM solutions.
• Web applications and browser systems

  • Testing PWAs and WebViews: offline mode, service workers, and native application wrappers.
  • Frameworks and DevTools for web testing, headless browsers, and CI automation.
  • Frontend performance and optimization: loading, rendering, caching.
  • Web application security: XSS, CSRF, SSRF, prompt injection via web forms, and supply chain attacks on test automation.
  • Web test automation, CI integration, headless browsers.
  • AI-assisted visual regression and screenshot testing.
• 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 data contracts: dbt tests, Great Expectations, especially for AI pipelines.
• 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.
• Observability-driven testing and testing in production

  • Chaos engineering and fault injection in production.
  • Production testing: canary releases, feature flags, and synthetic monitoring.
  • Checks based on metrics, traces, and logs.
  • SLO-driven testing and regression detection based on telemetry.