GITNUXSOFTWARE ADVICE
Business FinanceTop 10 Best Quality Check Software of 2026
Discover top quality check software to ensure flawless results. Compare features, find the best fit, and streamline your process today.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Xray
Requirements-to-test-to-execution traceability with automatic linkage across issues
Built for teams using Jira-style workflows for test management and traceability.
PractiTest
Traceability between requirements, test cases, and execution results
Built for teams needing traceability and evidence-driven test management with automation linkage.
Perfecto
Device cloud orchestration with real-device reservations and managed test execution
Built for qA teams needing real-device automation and orchestration across mobile and web.
Comparison Table
This comparison table evaluates quality check software used for test management, automation orchestration, and UI or integration validation across common delivery pipelines. It covers tools such as Xray, PractiTest, Perfecto, Browserless, and Diffblue, focusing on practical differences in workflows, automation capabilities, and integration patterns.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | Xray Uses Jira-native test management to execute and report quality check test cases and link test results to requirements and defects in finance delivery pipelines. | jira-native QA | 8.6/10 | 9.0/10 | 8.2/10 | 8.6/10 |
| 2 | PractiTest Coordinates quality check testing with test case management, traceability, and reporting across environments for regulated finance software workflows. | enterprise test management | 8.1/10 | 8.7/10 | 7.8/10 | 7.5/10 |
| 3 | Perfecto Runs mobile and web quality checks on cloud device labs with test automation and device coverage for finance app release validation. | device lab testing | 8.0/10 | 8.6/10 | 7.2/10 | 7.9/10 |
| 4 | Browserless Executes headless browser-based quality checks through an API for automated rendering, scraping verification, and regression validation in finance UIs. | API automation | 8.1/10 | 8.6/10 | 7.8/10 | 7.7/10 |
| 5 | Diffblue Generates and maintains unit tests to improve quality checks for Java and Spring code that supports finance platform reliability. | AI unit testing | 7.2/10 | 7.6/10 | 6.8/10 | 6.9/10 |
| 6 | Snyk Performs security quality checks with dependency scanning, vulnerability alerts, and code fixes that reduce risk in finance application stacks. | security QA | 8.1/10 | 8.6/10 | 7.8/10 | 7.6/10 |
| 7 | Mabl Provides AI-assisted web application test automation with continuous visual and functional checks. | AI test automation | 8.1/10 | 8.7/10 | 7.9/10 | 7.6/10 |
| 8 | Testim Automates web UI quality checks using script-light test creation with self-healing selectors. | AI UI testing | 8.2/10 | 8.7/10 | 7.9/10 | 7.8/10 |
| 9 | Cypress Runs fast end-to-end and component tests for web apps with interactive debugging and CI integration. | web testing | 8.4/10 | 8.7/10 | 8.6/10 | 7.8/10 |
| 10 | Playwright Automates cross-browser web UI checks with a code-first framework and strong CI support. | cross-browser testing | 7.9/10 | 8.3/10 | 7.8/10 | 7.4/10 |
Uses Jira-native test management to execute and report quality check test cases and link test results to requirements and defects in finance delivery pipelines.
Coordinates quality check testing with test case management, traceability, and reporting across environments for regulated finance software workflows.
Runs mobile and web quality checks on cloud device labs with test automation and device coverage for finance app release validation.
Executes headless browser-based quality checks through an API for automated rendering, scraping verification, and regression validation in finance UIs.
Generates and maintains unit tests to improve quality checks for Java and Spring code that supports finance platform reliability.
Performs security quality checks with dependency scanning, vulnerability alerts, and code fixes that reduce risk in finance application stacks.
Provides AI-assisted web application test automation with continuous visual and functional checks.
Automates web UI quality checks using script-light test creation with self-healing selectors.
Runs fast end-to-end and component tests for web apps with interactive debugging and CI integration.
Automates cross-browser web UI checks with a code-first framework and strong CI support.
Xray
jira-native QAUses Jira-native test management to execute and report quality check test cases and link test results to requirements and defects in finance delivery pipelines.
Requirements-to-test-to-execution traceability with automatic linkage across issues
Xray stands out for turning quality checks into structured issue workflows that teams can manage inside Jira-style environments. It supports creating test repositories, defining test execution cycles, and tracking results linked to requirements and defects. Its strength is operational quality control with traceability across tests, executions, and the issues raised from failed checks.
Pros
- Strong traceability between requirements, tests, executions, and defects.
- Flexible test management with reusable test cases and structured execution flows.
- Clear reporting for execution status, coverage trends, and defect outcomes.
Cons
- Setup complexity increases with deeper workflow customization and mappings.
- Advanced reporting often needs careful configuration of projects and fields.
- Non-Jira-centric teams may face integration and process friction.
Best For
Teams using Jira-style workflows for test management and traceability
PractiTest
enterprise test managementCoordinates quality check testing with test case management, traceability, and reporting across environments for regulated finance software workflows.
Traceability between requirements, test cases, and execution results
PractiTest stands out with a test management system that connects manual testing status to automated test execution evidence. It supports requirements traceability, test case structuring, and execution tracking tied to releases and test cycles. The platform emphasizes collaboration through centralized artifacts, structured test runs, and reporting for quality metrics. Automation integrations capture results into test executions so teams can review failures in context without spreadsheet workflows.
Pros
- Strong requirements-to-test traceability for audit-ready coverage
- Tight reporting across releases, test cycles, and execution statuses
- Automation results can be linked to test cases and runs
- Centralized evidence storage improves investigation and collaboration
- Configurable workflows support consistent execution across teams
Cons
- Setup for workflows and traceability structures can take time
- Advanced reporting can feel rigid without disciplined test modeling
- Navigation across large projects can slow down day-to-day triage
Best For
Teams needing traceability and evidence-driven test management with automation linkage
Perfecto
device lab testingRuns mobile and web quality checks on cloud device labs with test automation and device coverage for finance app release validation.
Device cloud orchestration with real-device reservations and managed test execution
Perfecto stands out for end-to-end mobile and web device quality testing using a large real-device cloud and strong test execution controls. It supports automation and manual testing workflows with device reservation, test orchestration, and integration into CI pipelines. The platform also includes visibility for debugging through logs and recordings, which shortens time from failure to diagnosis. Quality checks extend to responsive behavior and cross-device verification using automated and scripted test runs.
Pros
- Large real-device testing cloud with consistent cross-device execution
- Deep test orchestration features for reliable automation runs
- Failure diagnostics via logs and execution artifacts for faster triage
Cons
- Setup and environment tuning can require specialized QA engineering
- Debugging overhead increases with highly parallel device sessions
- Workflow complexity can slow teams without established test automation
Best For
QA teams needing real-device automation and orchestration across mobile and web
Browserless
API automationExecutes headless browser-based quality checks through an API for automated rendering, scraping verification, and regression validation in finance UIs.
Remote headless browser execution via API for Puppeteer-style quality workflows
Browserless provides an API for running real headless browser sessions to execute automated checks that can include DOM validation and visual inspection hooks. It supports driving Chromium with features such as remote debugging and scripts that can navigate, wait on selectors, and extract page state. Teams use it to offload browser execution from their own infrastructure and run quality checks reliably at scale.
Pros
- API-first remote Chromium execution for consistent automated quality checks
- Supports Puppeteer style workflows for navigation, selectors, and extraction
- Scales browser runs without managing browser infrastructure directly
- Deterministic automation patterns useful for regression and monitoring
Cons
- Requires code-based orchestration for checks and assertions
- Debugging failures can be harder than local browser runs
- Queueing and resource limits can affect turnaround under heavy loads
Best For
Teams needing API-driven visual and DOM quality checks at scale
Diffblue
AI unit testingGenerates and maintains unit tests to improve quality checks for Java and Spring code that supports finance platform reliability.
Diffblue Test Generation that creates and improves JUnit tests using code coverage signals
Diffblue stands out for generating automated JUnit tests from Java code and coverage feedback instead of writing test steps manually. It focuses on quality checking by creating runnable unit tests that exercise production logic and improve code coverage signals. The approach is strongest for Java-centric projects where deterministic test generation can reduce gaps in regression coverage. It is less suited for teams needing rich end-to-end test orchestration across many UI and service layers.
Pros
- Automatically generates runnable JUnit tests from existing Java code
- Uses coverage feedback to refine and extend generated tests
- Integrates into Java build flows to support continuous testing
Cons
- Test quality can require developer review for complex domain logic
- Best results depend on manageable code structure and testable dependencies
- Limited scope for non-Java quality checks and end-to-end scenarios
Best For
Java teams needing fast unit-test generation to raise coverage quality checks
Snyk
security QAPerforms security quality checks with dependency scanning, vulnerability alerts, and code fixes that reduce risk in finance application stacks.
Snyk Open Source dependency scanning with pull request and continuous monitoring
Snyk stands out for catching security quality issues in code, dependencies, and deployed containers with actionable remediation. Its core capabilities cover Snyk Code for source scanning, Snyk Open Source for dependency analysis, and Snyk Container for image scanning. It also ties findings to continuous workflows through integrations for CI pipelines and code repositories, with issue triage features that support remediation tracking. The platform is strongest at preventing known vulnerabilities from reaching builds and deployments.
Pros
- Combines code, dependency, and container scanning in one security quality workflow.
- Provides remediation guidance and links each finding to specific vulnerable components.
- Integrates with CI and repository workflows for automated scans on changes.
Cons
- Findings can be noisy across large dependency graphs without strong policies.
- Requires ongoing configuration to keep scans aligned with build and deployment realities.
- Focus on security risks means not all quality checks are covered.
Best For
Teams preventing dependency and container vulnerabilities before release builds
Mabl
AI test automationProvides AI-assisted web application test automation with continuous visual and functional checks.
ML-driven Smart Locator that adapts to DOM changes to prevent broken UI tests
Mabl stands out by using machine learning to reduce test brittleness as UI changes occur. It delivers automated quality checks through guided test creation, AI-assisted selectors, and scheduled regression runs across web apps. Core capabilities include end-to-end testing, visual assertions, environment-aware configuration, and integrations that push results into common CI and issue workflows.
Pros
- AI-assisted test creation reduces selector maintenance during UI changes
- End-to-end test coverage includes real user flows and robust assertions
- Visual validation and failure screenshots speed diagnosis and triage
- Integrations connect test runs to CI pipelines and reporting workflows
Cons
- Advanced customization can require mabl-specific learning beyond basic scripting
- Large suites need governance to keep runtime and signal quality under control
- Complex multi-system setup can add overhead for reliable environment configuration
Best For
Teams automating web app quality with low-maintenance end-to-end checks
Testim
AI UI testingAutomates web UI quality checks using script-light test creation with self-healing selectors.
AI test creation that generates resilient UI tests from recorded user interactions
Testim stands out for AI-assisted test creation that converts user flows into robust automated tests with less manual scripting. It supports web UI testing across browsers using a visual recorder, component-aware selectors, and data-driven test steps. Quality checks can run in CI pipelines with detailed execution reports that highlight failing steps, screenshots, and traces to speed root-cause analysis. Teams also gain control through reusable test plans, environments, and assertions tailored to dynamic interfaces.
Pros
- AI-assisted test generation speeds up coverage for common user journeys
- Recorder plus smart selectors reduce brittleness on dynamic UI changes
- CI-friendly execution with step-level failure details and artifacts
Cons
- Complex UI states still require manual refinement of test logic
- Selector tuning can be time-consuming for highly variable components
- Advanced customization needs scripting skill beyond pure visual authoring
Best For
Teams automating web UI quality checks with visual authoring and CI reporting
Cypress
web testingRuns fast end-to-end and component tests for web apps with interactive debugging and CI integration.
Real-time interactive runner with time-travel debugging and state inspection
Cypress stands out with real browser execution that supports interactive time-travel debugging during test development. It provides end-to-end and component testing using JavaScript test code, automatic waiting, and rich assertions. The runner records screenshots and network activity when tests fail, which accelerates defect localization. Its tight feedback loop makes it a strong quality check tool for UI regressions and cross-browser verification workflows.
Pros
- Interactive time-travel debugger pinpoints UI state changes and root causes quickly
- Automatic waiting reduces flaky timing issues in UI quality checks
- Built-in screenshots and video capture improve failure triage without extra tooling
- Network request stubbing enables deterministic UI tests for quality gates
- Component testing supports isolated verification of UI units and edge cases
Cons
- Focused primarily on browser-based testing, limiting coverage for non-UI quality checks
- Test organization can become complex at scale without strong suite conventions
- Parallelization and CI tuning often require additional setup to maintain throughput
- Mocking network behavior can drift from production if fixtures are not maintained
Best For
Teams needing fast UI regression quality checks with component and end-to-end coverage
Playwright
cross-browser testingAutomates cross-browser web UI checks with a code-first framework and strong CI support.
Trace Viewer with full action timeline and screenshots for diagnosing failing test steps
Playwright stands out with cross-browser end to end testing driven by a single codebase and a rich automation API. It supports reliable UI quality checks through auto waiting, network interception, and deterministic selectors for stable assertions. Headless and headed execution across Chromium, Firefox, and WebKit helps validate the same user flows across browser engines.
Pros
- Auto-waiting reduces flaky UI checks by syncing actions to actual readiness
- Cross-browser engine coverage validates the same flows on Chromium, Firefox, and WebKit
- Network interception enables assertions on requests, responses, and payloads
Cons
- Complex projects need strong test architecture to keep suites maintainable
- Debugging intermittent failures often requires inspecting traces and timelines
- UI quality checks can still flake when selectors target unstable markup
Best For
Teams needing reliable cross-browser UI quality checks with code-based automation
Conclusion
After evaluating 10 business finance, Xray stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Quality Check Software
This buyer's guide explains how to choose Quality Check Software that matches specific test types, workflows, and evidence requirements. It covers Xray, PractiTest, Perfecto, Browserless, Diffblue, Snyk, Mabl, Testim, Cypress, and Playwright with concrete selection criteria tied to their documented capabilities. The guide also maps common implementation pitfalls to the tools that are most affected so teams can avoid wasted effort.
What Is Quality Check Software?
Quality Check Software automates and structures quality verification activities like test execution, result tracking, and evidence capture across code, UI, devices, and security signals. It solves the problem of inconsistent checks and weak traceability by centralizing artifacts such as test cases, execution runs, and failure diagnostics. Teams use it to prevent defects from reaching release by linking checks to requirements, releases, executions, and findings. Tools like Xray implement requirement-to-test-to-execution traceability in Jira-style workflows. Tools like Cypress and Playwright execute fast web UI quality checks with detailed failure capture and debugging.
Key Features to Look For
The right quality check feature set determines whether teams can produce reliable pass and fail signals, complete traceability, and fast diagnosis under real workload.
Requirements-to-test-to-execution traceability
Look for end-to-end linkage between requirements, test cases, executions, and resulting issues so audits and investigations stay consistent. Xray is built around requirements-to-test-to-execution traceability with automatic linkage across issues. PractiTest also emphasizes traceability between requirements, test cases, and execution results for audit-ready coverage.
Evidence capture tied to automation runs and artifacts
Choose tools that store execution context and evidence so failing checks can be reviewed without chasing spreadsheets or logs. PractiTest connects manual testing status to automated test execution evidence. Perfecto provides logs and execution artifacts that shorten time from failure to diagnosis.
Real-device and orchestrated mobile and web execution
For mobile apps and responsive behavior validation, prioritize managed real-device execution and test orchestration. Perfecto delivers a real-device cloud with device reservation and managed test execution. It also supports cross-device verification for responsive behavior and finance app release validation.
API-driven headless browser execution at scale
Select API-based browser execution when quality checks must run as code-driven regression jobs without managing browser infrastructure. Browserless provides remote headless browser execution via API for Puppeteer-style quality workflows. It supports navigation, waiting on selectors, and page state extraction for DOM validation and visual inspection hooks.
AI-assisted UI test creation and resilience
Prefer tools that reduce UI brittleness so quality checks keep passing as markup and layouts change. Mabl includes ML-driven Smart Locator to adapt to DOM changes and prevent broken UI tests. Testim uses AI test creation and smart selectors to generate resilient UI tests from recorded user interactions.
Fast, developer-friendly UI diagnostics for failing steps
Pick tools that make failure triage fast by capturing state, timing, and step context automatically. Cypress provides an interactive time-travel debugger plus built-in screenshots and video capture on failures. Playwright adds a Trace Viewer with full action timelines and screenshots to diagnose failing test steps.
How to Choose the Right Quality Check Software
Selection should start with the exact quality signals needed, then match them to the tool architecture that produces reliable evidence and traceability.
Match the tool to the quality check type
For Jira-centric teams that must connect requirements to quality outcomes, Xray provides structured test execution flows with requirements-to-test-to-execution traceability and automatic linkage across issues. For teams that must coordinate traceability and evidence across releases and test cycles, PractiTest ties execution results back to test cases and requirements. For real-device mobile and web validation, Perfecto focuses on device cloud orchestration with real-device reservations and managed test execution.
Decide between code-based UI automation and record-and-author workflows
Teams seeking fast feedback and interactive developer debugging should evaluate Cypress for real browser execution with an interactive time-travel debugger. Teams needing cross-browser coverage across Chromium, Firefox, and WebKit should evaluate Playwright for a single codebase and deterministic auto-waiting behavior. Teams aiming for script-light automation with AI and visual recording should evaluate Testim for AI-assisted test creation and self-healing selectors, or Mabl for ML-driven Smart Locator.
Plan for scale, stability, and evidence quality
For API-driven regression checks that run headless browser sessions without owning browser infrastructure, Browserless supports remote Chromium execution via API for Puppeteer-style scripts and DOM validation. For teams scaling UI suites, prioritize built-in failure evidence like Cypress screenshots and video capture or Playwright Trace Viewer timelines. For highly dynamic UI that breaks selectors, prioritize resilience features like Mabl Smart Locator and Testim smart selectors.
Include non-UI quality signals when risk is part of the definition of quality
If security quality gates are part of the release definition, Snyk focuses on security quality checks with dependency scanning and container scanning tied to CI and repository workflows. If the main quality gap is Java unit coverage, Diffblue generates and maintains runnable JUnit tests from Java code using coverage feedback signals. If quality checks target web flows and ongoing visual regression confidence, Mabl and Testim provide scheduled regression runs with visual validation and step-level failure artifacts.
Validate workflow fit with traceability and environment structure
Jira-native workflow alignment strongly favors Xray because it uses Jira-style execution and reporting patterns and links test results to requirements and defects. For regulated finance workflows that depend on evidence-driven execution and structured test runs, PractiTest provides configurable workflows and centralized evidence storage. For teams that struggle with environment setup and test orchestration complexity, Perfecto and Browserless require QA engineering discipline for reliable reservations or selector-driven automation at scale.
Who Needs Quality Check Software?
Quality Check Software benefits teams that need repeatable pass and fail signals, structured evidence, and faster defect localization across code, UI, devices, and security risk.
Jira-centric quality and audit traceability teams
Xray excels for teams using Jira-style workflows that require requirements-to-test-to-execution traceability with automatic linkage across issues. PractiTest also fits teams that need traceability between requirements, test cases, and execution results with evidence tied to releases and test cycles.
Regulated finance teams needing audit-ready evidence and automation linkage
PractiTest is built around evidence-driven test management that connects manual testing status to automated test execution evidence. It also supports centralized artifacts that improve collaboration during failure investigation and reduce spreadsheet-style workflows.
Mobile and responsive finance app QA teams using real devices
Perfecto is designed for QA teams needing real-device automation and orchestration across mobile and web with device reservation. It adds logs and execution artifacts to shorten time from failure to diagnosis in complex device-dependent failures.
Web UI teams that want fast developer debugging and reliable UI regression
Cypress suits teams needing fast end-to-end and component tests with an interactive time-travel debugger and built-in screenshots and video capture. Playwright suits teams needing reliable cross-browser UI checks with deterministic auto-waiting and Trace Viewer timelines.
Common Mistakes to Avoid
Implementation missteps usually come from choosing a tool that cannot produce the needed traceability or evidence, or from adopting an automation approach that increases maintenance friction.
Choosing a UI tool but treating non-UI quality as out of scope
Cypress and Playwright focus primarily on browser-based testing, which can limit coverage for dependency or container security quality signals. Snyk fills that gap by combining Snyk Code, Snyk Open Source dependency scanning, and Snyk Container scanning with remediation guidance tied to CI and repositories.
Underestimating traceability and workflow modeling effort
Xray and PractiTest require deeper workflow customization and mappings to achieve consistent traceability across requirements, tests, and executions. Teams that skip disciplined test modeling risk rigid reporting patterns in PractiTest and more complex setup in Xray.
Expecting API headless automation to be maintenance-free without code orchestration
Browserless requires code-based orchestration for checks and assertions, which makes quality gates dependent on selector and script design. Teams should plan for harder debugging than local browser runs because remote headless failures can be less direct to reproduce.
Assuming AI UI automation eliminates every dynamic UI edge case
Mabl and Testim reduce brittleness using ML-driven Smart Locator and self-healing selectors, but complex UI states still require manual refinement of test logic. Failing to invest in governance can also reduce runtime and signal quality for large suites in Mabl.
How We Selected and Ranked These Tools
we evaluated Xray, PractiTest, Perfecto, Browserless, Diffblue, Snyk, Mabl, Testim, Cypress, and Playwright by scoring every tool on three sub-dimensions. Features had a weight of 0.40, ease of use had a weight of 0.30, and value had a weight of 0.30. The overall rating is the weighted average of those three sub-dimensions using overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. Xray separated itself from lower-ranked tools on the features dimension by delivering requirements-to-test-to-execution traceability with automatic linkage across issues, which directly supports structured issue workflows for quality checks in Jira-style environments.
Frequently Asked Questions About Quality Check Software
Which quality check software is best for requirement-to-test traceability without spreadsheet work?
Xray fits teams that need requirements-to-test-to-execution traceability inside Jira-style workflows. PractiTest also provides requirements traceability, but it emphasizes evidence capture that ties manual testing status to automated execution results.
What tool helps QA teams link failed quality checks to issue workflows with execution cycles?
Xray turns quality checks into structured issue workflows by linking test execution results to the issues raised from failed checks. PractiTest similarly ties executions to test cycles and releases, but its reporting centers on centralized artifacts and evidence review.
Which option is strongest for end-to-end mobile and web quality checks on real devices?
Perfecto is built for real-device quality testing with device reservation, orchestration, and CI pipeline integration. It supports both automation and manual workflows and includes debugging visibility through logs and recordings.
Which quality check software runs browser-based checks at scale through an API?
Browserless provides an API for executing real headless Chromium sessions that can validate DOM state and support visual inspection hooks. It’s designed to offload browser execution from team infrastructure while keeping workflows Puppeteer-style.
How do teams reduce brittle UI checks caused by frequent DOM changes?
Mabl uses machine learning Smart Locator to adapt to DOM changes and prevent broken UI tests. Testim also improves resilience by generating UI tests from recorded flows and using component-aware selectors for dynamic interfaces.
Which tool is best for AI-assisted test creation from user flows with CI-ready execution reports?
Testim uses AI-assisted conversion of user flows into robust automated tests with visual recording. Its CI execution reports highlight failing steps with screenshots to speed root-cause analysis.
Which quality check software is best for fast interactive debugging of UI regressions?
Cypress provides an interactive runner with time-travel debugging during test development. It records screenshots and network activity on failures, which helps teams pinpoint the exact state that caused the UI regression.
Which option is strongest for stable cross-browser UI quality checks from a single automation codebase?
Playwright supports cross-browser end-to-end testing from one codebase across Chromium, Firefox, and WebKit. Its auto waiting, network interception, and trace viewer action timeline help stabilize and diagnose quality checks.
Which quality check software is focused on security quality checks for code, dependencies, and containers?
Snyk concentrates on security findings across source code, open-source dependencies, and container images. It integrates into CI and repository workflows so teams can triage findings and track remediation rather than treating security as a post-release step.
Tools reviewed
Referenced in the comparison table and product reviews above.
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