7 Automation Tester Interview Questions and Answers

Introduction

This question assesses your technical expertise in automation testing, which is crucial for a QA Automation Engineer. Understanding your practical experience with tools helps gauge your ability to contribute effectively right from the start.

How to answer

• Start by naming the specific automation testing tools you've used (e.g., Selenium, TestNG, JUnit).
• Describe the context of the projects where you applied these tools.
• Explain your role in implementing the automation framework, including any challenges faced.
• Discuss the results achieved through automation, such as reduced testing time or increased test coverage.
• Mention any continuous integration (CI) tools you integrated with your automation process.

What not to say

• Vaguely mentioning automation without specific tools or examples.
• Focusing solely on theoretical knowledge without practical application.
• Neglecting to discuss the outcomes or benefits of your automation efforts.
• Claiming experience with tools you are not proficient in.

Example answer

“In my previous role at TCS, I extensively used Selenium WebDriver for automating regression tests. I implemented a hybrid automation framework that reduced our testing cycle by 40%. By integrating it with Jenkins, we successfully achieved continuous testing, which helped us release features faster without compromising quality.”

Introduction

This question evaluates your analytical and problem-solving skills, which are essential for identifying and resolving issues in software development.

How to answer

• Use the STAR method to structure your response.
• Clearly outline the nature of the bug and its impact on the project.
• Detail the steps you took to reproduce the bug and gather information.
• Explain how you collaborated with the development team to address the issue.
• Discuss the outcome and any preventative measures you implemented to avoid similar bugs in the future.

What not to say

• Focusing on minor bugs without demonstrating significant problem-solving.
• Blaming others for the bug without taking any accountability.
• Not mentioning the resolution process or what you learned from it.
• Avoiding details about the collaboration with other teams.

Example answer

“During my time at Infosys, I discovered a critical bug in the payment processing system that caused incorrect transaction amounts. I documented the steps to reproduce it and worked closely with the development team to identify the root cause, which turned out to be a misconfiguration in the API. Together, we implemented a fix and added additional automated tests to catch similar issues in the future, increasing our testing coverage by 30%.”

Introduction

This question assesses your problem-solving skills and ability to handle complex testing situations, which are critical for an Automation Test Engineer.

How to answer

• Use the STAR method (Situation, Task, Action, Result) to structure your response
• Clearly define the testing scenario and why it was challenging
• Explain the steps you took to understand and resolve the issue
• Highlight any tools or frameworks you utilized to address the problem
• Discuss the outcome and what you learned from the experience

What not to say

• Avoid vague descriptions without context or specifics
• Don't focus solely on technical details without explaining your thought process
• Refrain from shifting blame to others instead of taking ownership
• Avoid failing to mention the learning outcome from the experience

Example answer

“In my previous role at Grab, I encountered a scenario where our automated tests frequently failed due to inconsistent test data. I analyzed the data generation process and discovered that the setup scripts were not populating the database correctly. I collaborated with the development team to create a more stable data generation approach and implemented additional validation checks. As a result, test reliability improved by 80%, which significantly reduced our regression test cycle time.”

Introduction

This question evaluates your technical knowledge and familiarity with automation tools, which are vital for the role of an Automation Test Engineer.

How to answer

• List the automation tools and frameworks you have used, such as Selenium, TestNG, or Appium
• Explain your rationale for choosing these tools based on project needs
• Discuss your experience with integration into CI/CD pipelines
• Include any relevant certifications or trainings related to these tools
• Highlight the benefits you observed while using these tools in your projects

What not to say

• Avoid mentioning outdated or irrelevant tools without context
• Don't express preference for tools without explaining why
• Refrain from claiming expertise in tools you have minimal experience with
• Avoid making negative comparisons without constructive insights

Example answer

“I have extensive experience with Selenium and TestNG for web applications, and I prefer them due to their robust support for parallel test execution and easy integration with Jenkins for CI/CD. At my last job with Singapore Airlines, I successfully implemented a Selenium-based test suite that reduced manual testing efforts by 60% while improving test coverage. Moreover, I completed a certification on Selenium WebDriver to deepen my expertise.”

Introduction

This question assesses your attention to detail, problem-solving skills, and ability to communicate effectively with your team when dealing with defects.

How to answer

• Start by briefly describing the project and the context of the testing phase.
• Clearly explain the defect you discovered and why it was critical.
• Detail the steps you took to analyze the defect and document it.
• Discuss how you communicated the issue with your team and stakeholders.
• Share the outcome and any lessons learned from the experience.

What not to say

• Ignoring the importance of communication with the team.
• Downplaying the impact of the defect on the project.
• Failing to explain the steps taken to resolve the issue.
• Not reflecting on the lessons learned from the experience.

Example answer

“In my role at Accenture, I discovered a critical defect in an e-commerce application during regression testing. This defect caused incorrect pricing to be displayed under certain conditions. I documented the issue in our tracking system and immediately communicated it to the development team. We prioritized the fix, and I helped coordinate a retest post-fix. Ultimately, we resolved the issue before launch, preventing potential revenue loss. This experience highlighted the importance of thorough documentation and proactive communication.”

Introduction

This question gauges your technical knowledge and familiarity with tools that enhance automation testing efficiency.

How to answer

• Name specific tools you have experience with, such as Selenium, JUnit, or TestNG.
• Explain the features of these tools that make them valuable.
• Discuss your criteria for selecting tools based on project needs.
• Share any experiences where a specific tool significantly improved the testing process.
• Mention any emerging tools you are interested in exploring.

What not to say

• Listing tools without explaining their benefits.
• Claiming familiarity with every tool without specifics.
• Neglecting to mention your hands-on experience with tools.
• Ignoring the importance of selecting tools based on project requirements.

Example answer

“I consider Selenium and JUnit essential for automation testing due to their robustness and community support. Selenium allows for thorough cross-browser testing, which is critical for our web applications, while JUnit provides a strong framework for unit testing in Java. In a recent project at Deloitte, using Selenium reduced our test execution time by 40%, which significantly sped up our release cycles. I’m also eager to explore newer tools like Cypress for its modern approach to testing.”

Introduction

Senior automation testers must design scalable, maintainable frameworks that support multiple teams, integrate with CI/CD pipelines, and satisfy regulatory and security requirements common in Australian financial services.

How to answer

• Start with the high-level goals: scalability, maintainability, reliability, speed, security and compliance (e.g. APRA guidance).
• Propose an architecture (e.g. layered framework separating test runner, page/feature objects, service/API wrappers, test data and test orchestration).
• Specify tools and justify choices (e.g. Playwright/Selenium/WebDriverIO for UI; REST-assured/Postman or HTTP client for APIs; Jest/Mocha/Pytest as test runners; Docker for containerised test environments).
• Describe CI/CD integration (e.g. GitHub Actions/GitLab CI/Jenkins pipeline stages for linting, unit tests, parallel test execution, risk-based test gating and artifact reporting).
• Explain test data and environment strategy (mocking/stubbing sensitive data, using test sandboxes, contract testing, and data seeding with GDPR/Australian privacy considerations).
• Detail reliability practices: retries vs flakiness fixes, test isolation, parallelisation strategy and flaky-test dashboards.
• Cover reporting, metrics and observability (test results, pass/fail trends, test coverage, mean time to repair flaky tests) and stakeholder access to results.
• Address governance and cross-team adoption: shared libraries, contribution guidelines, code reviews, and training/mentoring for teams adopting the framework.

What not to say

• Listing tools without explaining the rationale or trade-offs for the business context.
• Proposing only UI tests or only end-to-end tests; ignoring API/unit test layers.
• Relying heavily on test retries as the primary fix for flaky tests instead of root-cause remediation.
• Ignoring security, data privacy or environment management requirements that are crucial in finance.

Example answer

“I'd design a modular framework using Playwright for UI (for cross-browser and headless support) and pytest for API/unit tests. The framework would implement Page/Component objects, shared utilities and a contract-testing layer for services. Tests run in Docker-based agents orchestrated by GitLab CI with parallelisation by feature area. Sensitive data is masked and sandbox environments seeded using a data-management pipeline; contract tests run on every merge to detect breaking changes early. We expose results via Allure and an internal dashboard tracking flaky-test rate and execution time. To ensure adoption across teams, I'd create a shared automations library, contribution templates, and run training sessions. This balance meets our reliability, speed and compliance needs for a financial product operating in Australia.”

Introduction

This behavioural/leadership question evaluates your ability to influence stakeholders, manage change, and deliver measurable improvements in automation coverage and quality — critical for senior testers leading automation at scale in Australian tech teams.

How to answer

• Use the STAR method: set the Situation and specific Task you faced.
• Explain the Actions you took to build consensus (workshops, demos, pilot projects, metrics) and the leadership or facilitation approach you used.
• Quantify Results (coverage increase, defect escape reduction, cycle time improvements) and describe lasting process changes.
• Highlight how you addressed resistance (training, pairing sessions, documenting benefits, aligning to product goals) and how you measured success.
• Mention any mentoring, governance changes, or living documentation you introduced to sustain adoption.

What not to say

• Claiming you solved it alone without acknowledging collaboration or trade-offs.
• Providing vague outcomes like 'coverage improved' without metrics or timelines.
• Focusing only on technical solutions while ignoring people and process aspects.
• Describing imposition of automation without stakeholder engagement or training.

Example answer

“At a fintech in Melbourne, test automation coverage for critical payment flows was under 20% and deployments were risky. I initiated a cross-functional pilot with one product area: ran a workshop to map risks, built CI-backed API and UI smoke suites, and paired QA with two backend engineers to co-author tests. I also created a simple ROI dashboard showing reduced manual regression hours and quicker deploy confidence. Within three months, coverage for the module rose to 70%, release rollback rate dropped by 40%, and developers adopted the shared test library. To scale, I wrote contribution guidelines and ran fortnightly drop-in sessions. This collaborative, data-driven approach secured long-term buy-in.”

Introduction

This situational/competency question checks your problem-solving, triage, and pragmatic decision-making when automation stability threatens delivery pipelines — a common scenario for senior testers in continuous-delivery environments.

How to answer

• Describe immediate containment steps to reduce release impact (e.g. quarantine flaky tests, mark as flaky/wip, or run affected suites after release).
• Explain how you'd gather data: failure rates, logs, screenshots, environment differences and timing to identify patterns.
• Outline root-cause analysis steps: reproduce locally, add verbose logging, check test isolation and timing assumptions, inspect network/timeouts and race conditions.
• Prioritise fixes: quick wins (timeouts, waits, selectors) vs longer-term changes (re-architect tests, mock unstable services).
• Cover process changes to prevent recurrence: flaky-test tracking board, quality gates refinement, improved test ownership and automated stability metrics in CI.
• Mention how you'd communicate with stakeholders and propose temporary mitigations (feature toggles, selective rollback, or canary releases) to keep business continuity.

What not to say

• Suggesting to ignore flaky tests or permanently skip large suites without remediation.
• Relying solely on increasing retry counts as the main solution.
• Failing to involve developers or product owners when flaky tests point to application instability.
• Not providing a clear plan to prevent recurrence.

Example answer

“First, I'd reduce release risk by quarantining the top flaky tests from the fast CI gate and running them in a secondary nightly pipeline, so daily releases can proceed. Simultaneously, I'd collect failure artifacts (screenshots, logs, timestamps) and prioritise tests by failure frequency and business impact. For each high-impact flaky test, I'd attempt local repro, then fix root causes — often replacing brittle CSS selectors, removing implicit waits, or mocking unstable backend calls. For deeper issues I’d coordinate with developers to fix race conditions. Finally, I’d add this to a flaky-test dashboard with SLAs for remediation and update CI quality gates to prevent regressions. I’d communicate the temporary changes and timeline to product and release managers to maintain transparency.”

Introduction

Automation Testers must design scalable, maintainable frameworks that meet functional, performance and regulatory needs (e.g., LGPD) for high-traffic applications. This question checks technical design, tool selection, and trade-off reasoning.

How to answer

• Start with high-level goals: reliability, scalability, maintainability, speed, traceability and compliance (LGPD/data masking).
• Propose an architecture (e.g., layered: test harness, page/interaction objects, data layer, test orchestration, reporting).
• Name specific tools and justify them for the Brazil market: e.g., Selenium or Playwright for web UI, Cypress where applicable, REST-assured or Postman/Newman for APIs, JUnit/TestNG or Jest/Mocha based on language, Allure or ReportPortal for reporting, Jenkins/GitLab CI/GitHub Actions for CI, Docker for environment parity, BrowserStack/Sauce Labs or local Selenium Grid for cross-browser.
• Explain test types and placement: unit vs integration vs API vs E2E; favor API tests for stability and speed, keep E2E for critical flows (login, transfers).
• Describe data strategy: test data generation, use of synthetic data, anonymization or masking to comply with LGPD, secrets management (Vault/credentials store).
• Describe test environment strategy: ephemeral environments via Docker/k8s, use of feature flags, environment provisioning in CI pipelines.
• Discuss maintainability: page object or screen object patterns, reusable helpers, clear naming, test tagging, flaky test handling and retry policies (limited, with root-cause tracking).
• Include metrics & monitoring: pass/fail trends, test duration, flakiness rate, coverage of critical business flows, integration with Jira for failed test tickets.
• Address performance and scalability: separation of functional vs load tests, use of JMeter/Gatling for load, integration with CI gating strategy.
• Conclude with rollout and governance: code reviews for tests, test ownership, documentation, and training for the QA/dev teams.

What not to say

• Listing tools without explaining why they fit the context (e.g., scaling to millions of users or LGPD needs).
• Proposing only UI E2E tests for everything — ignoring API/unit testing tradeoffs.
• Ignoring test data/privacy regulations relevant in Brazil (LGPD).
• Overcomplicating with unnecessary technologies that increase maintenance burden without benefit.
• Not addressing CI/CD integration or how tests will run reliably in pipelines.

Example answer

“For a high-traffic Brazilian banking web app, I'd adopt a layered framework: use Playwright for browser automation (fast parallel runs and solid cross-browser support) and REST-assured for API tests. Tests live in a Maven/Gradle project with JUnit 5 for orchestration. Page Object / Domain Action abstractions reduce duplication. CI runs on GitLab CI with Docker-based runners to create reproducible environments; critical E2E flows run nightly plus on release branches, while API tests run on every PR to catch regressions fast. Test data is synthetic and masked; any production-like data is anonymized to meet LGPD. Reports go to Allure and failures create Jira tickets with screenshots/video recordings stored in secured object storage. We track flakiness and require root-cause triage for tests that fail >2 times. This balances speed, reliability, and compliance while enabling the team to scale test coverage efficiently.”

Introduction

Flaky tests reduce confidence and slow delivery. This situational question evaluates debugging, prioritization, and process-improvement skills specific to automation testing in a team environment.

How to answer

• Frame your approach: triage, root-cause analysis, short-term mitigation, and long-term fixes.
• Describe how you'd gather data: test failure logs, screenshots/videos, environment metrics, parallelization patterns, and timing of failures (peak hours).
• List likely causes to rule out systematically: environment resource exhaustion, network instability, test timing/race conditions, shared test data collisions, or external service rate limits.
• Explain short-term mitigations to unblock the team: quarantine flaky tests (mark as flaky/unstable), run critical tests as a gated subset, or schedule heavy test runs off-peak.
• Detail long-term fixes: stabilize selectors/wait strategies, improve test isolation (unique test data, cleanup steps), add retries with logging only after root-cause identification, and provision more stable/isolated environments (dedicated runners, scale infrastructure).
• Discuss collaboration: involve devops to inspect runners and infrastructure, developers to inspect app logs, and product owners to prioritize critical user journeys to keep gated.
• Mention monitoring and prevention: add flakiness dashboards, automated alerts, and require a fix plan for tests failing repeatedly.

What not to say

• Immediately deleting or permanently skipping flaky tests without investigation.
• Relying solely on retries as a long-term solution.
• Blaming the CI or tools without data or collaboration with DevOps/development.
• Focusing only on technical fixes and ignoring process changes or team communication.

Example answer

“First I'd triage by collecting failures, screenshots and runner metrics to see patterns — since failures spike at peak hours, I suspect resource exhaustion or contention. I'd pause non-critical E2E suites during peak times and create a 'critical smoke' pipeline to unblock merges. Simultaneously, I'd quarantine consistently flaky tests and add richer logging to reproduce issues locally. Working with DevOps, we'd scale runners or provide isolated Dockerized environments to reduce contention. For tests failing due to timing, I'd replace brittle waits with stable wait-for conditions and unique test data. Finally, I'd add a flakiness dashboard and require a remediation plan for any test that flakes more than twice in a week. This minimizes team disruption while driving permanent fixes.”

Introduction

Automation Testers often need to influence engineers and product managers to adopt or expand automation. This behavioral/leadership question assesses persuasion, metrics-driven arguments, and cross-functional collaboration.

How to answer

• Use the STAR format: Situation, Task, Action, Result.
• Describe the context clearly (e.g., manual regression caused delayed releases at a fintech in São Paulo).
• Explain objectives (reduce release cycle time, improve quality, reduce manual QA effort).
• Detail your actions: gathering data (defect trends, release delays), building a prioritized automation roadmap focused on high-risk flows, creating cost/benefit estimates, and running a pilot to demonstrate value.
• Highlight communication tactics: demos, stakeholder-specific metrics (time saved for product managers, fewer hotfixes for developers), and alignment with business goals.
• Quantify results: reduced regression time, fewer production incidents, faster release cadence, ROI figures if possible.
• End with lessons learned about sustaining momentum and ownership.

What not to say

• Claiming victory without measurable outcomes or team buy-in.
• Taking sole credit without recognizing cross-functional effort.
• Describing convincing stakeholders through pressure rather than evidence and pilots.
• Providing vague or generic anecdotes without concrete impact.

Example answer

“At a mid-sized payments company in Brazil, frequent manual regression testing delayed monthly releases. I collected data showing an average of 10 hours/week QA per release and three post-release hotfixes per quarter. I proposed automating the top 10 critical flows (login, payments, balance checks) and ran a two-week pilot automating two flows. The pilot cut regression execution time by 60% and exposed several defects earlier. Using these results, I presented an ROI to product and engineering, showing reduced release time and lower incident cost. Leadership approved a six-month roadmap; after implementation, we decreased release cycle time by 30% and cut production incidents by 25%. Key to success was small wins, transparent metrics, and assigning owners to maintain the suite.”

Introduction

Junior automation testers must demonstrate practical test design, tool familiarity, and judgment about what to automate. Web login flows are common, and handling conditional elements (like CAPTCHA after failures) shows understanding of edge cases and maintainable automation.

How to answer

• Start with a concise scope: outline the positive and negative login scenarios, edge cases, and non-functional checks (e.g., performance or security if relevant).
• List test cases to automate (e.g., successful login, invalid password, lockout behavior, session timeout) and those to leave manual (e.g., CAPTCHA verification, visual accessibility checks).
• Explain test data strategy: use parameterized inputs, reset test accounts or use a dedicated test environment, and isolate tests to avoid shared-state flakiness.
• Choose tools and frameworks appropriate to the stack (e.g., Selenium or Playwright with Java/Python/JavaScript; pytest or JUnit; GitLab CI or Jenkins for CI in a French or EU context).
• Provide a short, readable script/snippet (pseudo-code or real) that demonstrates logging in, asserting success, and a loop to simulate password failures up to the CAPTCHA trigger.
• Describe how to integrate the test into CI: run smoke tests on each merge request, schedule full suites nightly, and report failures to the team.
• Mention maintainability: use page objects or similar patterns, keep selectors resilient, and add meaningful logs/screenshots on failure.

What not to say

• Claiming you'd automate everything including CAPTCHA or extensive visual/manual-only checks without explaining limitations.
• Providing only high-level ideas without a concrete script or clear test cases.
• Ignoring test data isolation and suggesting tests run against shared production accounts.
• Using brittle selectors (absolute XPaths) or omitting CI integration and reporting strategy.

Example answer

“Scope: automate core login flows: successful login, invalid username, invalid password, account lockout after 3 failures, session expiry. Manual: CAPTCHA verification (requires human verification) and detailed accessibility checks. Test cases to automate: 1) valid credentials -> assert redirect to dashboard and presence of user name; 2) invalid password -> assert error message; 3) repeat invalid password 3 times -> assert CAPTCHA appears or account locked state; 4) logout -> assert session cleared. Tooling: Selenium WebDriver with Python (pytest), page object pattern, GitLab CI to run smoke tests on PRs. Sample script (Python/pytest pseudo-code): - LoginPage.login(username, password) - assert DashboardPage.is_displayed() - For i in range(3): LoginPage.login(test_user, wrong_pw) - assert LoginPage.captcha_is_displayed() # stop automation here; manual step required Integration: run smoke tests on each merge request; nightly run for full auth suite; save screenshots on failure and post results to Slack. This balances reliable automation of deterministic flows while avoiding brittle CAPTCHA automation; tests are isolated by resetting test user state between runs.”

Introduction

Flaky tests are a common real-world problem that reduce trust in automation. Even junior testers should be able to investigate causes, communicate impact, and implement fixes or mitigations.

How to answer

• Use the STAR structure (Situation, Task, Action, Result) to organize your response.
• Start by describing the context: what system, which tests, and how flakiness manifested (random failures, timeouts, environment-dependent issues).
• Explain diagnostic steps: check logs, reproduce locally, run tests in isolation, compare CI vs local, inspect screenshots and stack traces, and consider timing/race conditions or environment instability (network, test data).
• Detail technical fixes you applied: added waits or replaced brittle sleeps with explicit waits, stabilized selectors, reset test data before each run, containerize dependencies, or mock external services.
• Describe process changes: quarantine flaky tests, add flakiness metrics, improve triage workflow with developers, or add flaky-test tags in CI to avoid blocking pipelines.
• Quantify results if possible: reduction in false failures, improved CI pass rate, or regained team trust.
• If you don’t have direct experience, describe a plausible, stepwise plan you would follow.

What not to say

• Blaming the CI or developers without evidence or steps you took to investigate.
• Saying you ignored flaky tests or disabled the suite permanently.
• Describing only superficial changes (e.g., increasing timeouts globally) without targeted fixes.
• Failing to mention communication with the team about impact and mitigations.

Example answer

“Situation: At a previous internship with a Capgemini project in France, our regression suite started failing intermittently in GitLab CI—about 10% of runs had unrelated failures. Task: I needed to restore confidence in the suite and reduce noise so developers wouldn’t ignore real regressions. Action: I triaged failures for a week, reproduced failures locally, and found many were due to timing/race conditions and shared test data collisions. I replaced fixed sleeps with explicit waits, used resilient selectors (data-test-id attributes), and introduced a test-data reset step using API calls before each test. I also containerized dependent services to ensure environment parity and added a flaky-tests label so non-deterministic tests would not block pipelines. Result: CI false-failure rate dropped from ~10% to under 2% in three weeks. Developers began trusting the pipeline again, and the team adopted a policy to fix or quarantine any test that failed more than twice in a row. This experience taught me the importance of methodical diagnosis, small targeted fixes, and clear team communication.”

Introduction

Junior testers need to prioritize work and create flexible automation strategies when product discovery and rapid change are happening. This question tests prioritization, risk assessment, and design for maintainability.

How to answer

• Start by identifying high-risk, high-value areas: core business flows, critical APIs, and stability points that would cause a production outage if broken.
• Prefer API-level tests for microservices: they are faster, less flaky, and easier to maintain while the UI and contracts are in flux.
• Automate stable, deterministic acceptance criteria (e.g., health endpoints, auth, core endpoints) and keep fragile/rapidly-changing features out of the critical path until stabilized.
• Use contract testing (e.g., Pact) to validate interactions between services and prevent regressions across teams.
• Design tests to be data-independent: seed and tear down test data via APIs or use isolated test environments (Docker Compose, Kubernetes namespaces).
• Adopt modular test architecture: small, focused tests; reusable helpers; configuration-driven endpoints; and mocks for external dependencies.
• Integrate tests into CI with quick smoke checks on merge requests and a broader suite nightly. Communicate with developers to adjust tests as APIs change.
• Plan a maintenance cadence: review failing tests immediately, refactor shared helpers regularly, and avoid over-automation of unstable areas.

What not to say

• Automating everything immediately instead of prioritizing high-value tests.
• Relying only on UI tests for a microservice-heavy architecture.
• Not using contract tests or failing to coordinate with developers about API changes.
• Neglecting test environment isolation and causing interference with other teams.

Example answer

“First, I'd focus on automating the service health checks and 3–5 core API endpoints that represent the main business capability—these are quick to run, deterministic, and catch critical regressions. I would write API-level tests using pytest (or JUnit) and run them in GitLab CI on each merge request as smoke tests. For inter-service dependencies, I'd introduce contract testing with Pact so front-end or downstream teams can detect breaking changes early. Because the service is evolving, I'd keep tests small and configuration-driven (base URL, auth tokens), mock external third-party APIs, and use dedicated test environments (Docker Compose or a namespaced cluster). Nightly runs cover broader scenarios, and the team agrees to fix or quarantine any test that starts failing frequently. This approach balances quick feedback with maintainability while the service matures.”