Testing, Quality & Reliability Topics
Quality assurance, testing methodologies, test automation, and reliability engineering. Includes QA frameworks, accessibility testing, quality metrics, and incident response from a reliability/engineering perspective. Covers testing strategies, risk-based testing, test case development, UAT, and quality transformations. Excludes operational incident management at scale (see 'Enterprise Operations & Incident Management').
Testing Strategy and Continuous Improvement
Covers high level strategic thinking about testing and how testing practices evolve over time. Topics include defining testing philosophy and strategy beyond individual frameworks or tools, test coverage planning, trade offs between test types, integrating testing into development lifecycle, establishing metrics for test effectiveness, and driving organization wide continuous improvement of testing practices and quality engineering processes.
Non Functional Testing
Knowledge of performance, scalability, reliability, accessibility, and security testing practices for modern applications. Candidates should describe approaches and tooling for load and stress testing, performance profiling, capacity planning, latency and throughput measurement, creating realistic load harnesses, and interpreting performance results. Accessibility testing and compliance with Web Content Accessibility Guidelines should be covered, as should security testing practices including the Open Web Application Security Project top ten, static and dynamic analysis, and basics of penetration testing. Candidates should also explain how to integrate non functional checks into continuous integration and continuous delivery pipelines and use performance budgets and monitoring to maintain quality.
Testing Related Problem Solving
Solve problems in contexts adjacent to software testing and validation, such as generating test data combinations, designing validation logic for API responses, detecting anomalies in test results, or writing small algorithmic solutions that support quality assurance. Assess systematic thinking about edge cases, combinatorial test coverage, input generation strategies, and pragmatic trade offs between exhaustive testing and practicality. Expect short technical exercises or algorithmic prompts framed as testing tasks that evaluate coding clarity, correctness, and test oriented reasoning.
Test Automation Framework Architecture and Design
Design and architecture of test automation frameworks and the design patterns used to make them maintainable, extensible, and scalable across teams and applications. Topics include framework types such as modular and structured frameworks, data driven frameworks, keyword driven frameworks, hybrid approaches, and behavior driven development style organization. Core architectural principles covered are separation of concerns, layering, componentization, platform abstraction, reusability, maintainability, extensibility, and scalability. Framework components include test runners, adapters, element locators or selectors, action and interaction layers, test flow and assertion layers, utilities, reporting and logging, fixture and environment management, test data management, configuration management, artifact storage and versioning, and integration points for continuous integration and continuous delivery pipelines. Design for large scale and multi team usage encompasses abstraction layers, reusable libraries, configuration strategies, support for multiple test types such as user interface tests, application programming interface tests, and performance tests, and approaches that enable non automation experts to write or maintain tests. Architectural concerns for performance and reliability include parallel and distributed execution, cloud or container based runners, orchestration and resource management, flaky test mitigation techniques, retry strategies, robust waiting and synchronization, observability with logging and metrics, test selection and test impact analysis, and branching and release strategies for test artifacts. Design patterns such as the Page Object Model, Screenplay pattern, Factory pattern, Singleton pattern, Builder pattern, Strategy pattern, and Dependency Injection are emphasized, with guidance on trade offs, when to apply each pattern, how patterns interact, anti patterns to avoid, and concrete refactoring examples. Governance and process topics include shared libraries and contribution patterns, code review standards, onboarding documentation, metrics to measure return on investment for automation, and strategies to keep maintenance costs low while scaling to hundreds or thousands of tests.
Collaboration with Development Teams on Quality Issues
Be prepared to discuss how you work with developers when reporting bugs, verifying fixes, and discussing quality improvements. Explain how you communicate effectively with non-QA team members, ask clarifying questions about expected behavior, and work together to ensure quality standards are met. Share an example of a time you collaborated with a developer to understand a complex issue or verify a fix.
Test Scenario Identification and Analysis
Ability to derive comprehensive and prioritized test scenarios from feature descriptions or requirements. Includes identification of positive paths, negative paths, boundary and edge cases, error conditions, and performance or security related scenarios. Covers risk based prioritization, test case design techniques, and how to document scenarios so they are actionable for manual or automated testing.
Mocking, Stubbing, and Test Isolation
Techniques for isolating tests from external dependencies using mocks, stubs, and test doubles. Understanding when to mock vs. when to use real services, and how to make tests reliable while still validating real behavior.
Scalability and Load Testing
Designing, executing, and interpreting performance and scalability tests for systems that must handle high traffic and large data volumes. Topics include creating realistic user and traffic patterns, ramp up strategies, steady state and stress scenarios, endurance and spike testing, and methods to identify breaking points, failure modes, and nonlinear bottlenecks. Covers test types such as load testing, stress testing, performance testing, chaos engineering, and multi region testing under degraded network and failure conditions, as well as testing with realistic data volumes. Emphasizes instrumentation and observability best practices, including which metrics to collect such as latency percentiles, throughput, error rates, and resource utilization, and how to interpret those metrics to find bottlenecks and derive capacity plans and autoscaling policies. Discusses graceful degradation and fault tolerance strategies, fault injection and chaos experiments, test automation and orchestration, test environment fidelity and realistic data generation or masking, avoiding false positives from unrealistic setups, and identifying and removing performance bottlenecks in the test harness itself. Includes practical considerations for optimizing test execution for cost and speed and using test outcomes to inform system design, operational runbooks, and production readiness.
Performance and Load Testing
Covers design and execution of tests that measure how software behaves under varying levels of user concurrency and resource demand, including load testing, stress testing, soak testing, and spike testing. Includes key performance metrics such as response time, throughput, latency, error rates, and resource utilization and how to collect and interpret these signals. Explains common tooling and approaches for load generation and results analysis, for example JMeter, Gatling, and LoadRunner, and how to instrument systems for monitoring and tracing. Addresses testing at scale, including distributed load generation, test environment configuration, test data management, and identifying and diagnosing performance bottlenecks across application, database, and infrastructure layers. Describes how to integrate performance testing into the development lifecycle and continuous integration and continuous delivery pipelines, how to report findings and performance regressions to stakeholders, and how functional correctness concerns interact with performance objectives.