GITNUXSOFTWARE REVIEW
Science ResearchTop 10 Best IT Testing Software of 2026
Compare it testing software options and what each supports for teams, with practical tool notes to help shortlist Percy, Postman, and Applitools.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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Percy is the best overall choice for QA teams who need CI-integrated visual regression review because its pull-request focused diffs tie directly to build artifacts and route approvals to the UI change reviewers, while Postman fits teams testing API regressions in automation-friendly collections thanks to reusable request artifacts with embedded test scripts.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Percy
Pull-request focused visual review with build-linked diffs that route approvals to UI change reviewers.
Best for: Fits when QA teams need CI-integrated visual regression review for fast UI change approvals.
Postman
Editor pickCollection runs with embedded pre-request and test scripts provide executable assertions per request.
Best for: Fits when QA teams need API-focused regression suites with CI automation and reusable request artifacts.
Applitools
Editor pickVisual AI-driven comparisons generate actionable UI diffs from rendered output, not DOM snapshots.
Best for: Fits when teams need reliable UI regression evidence with visual diffs in CI.
Comparison Table
Percy
specialistVisual regression testing service for web UI changes integrated into development pipelines.
Pull-request focused visual review with build-linked diffs that route approvals to UI change reviewers.
Percy from BrowserStack focuses on visual regression testing with automated image diffs and review workflows for UI changes. It integrates with major CI pipelines and common test stacks so visual checks run alongside functional regression suites.
Percy also supports branch-based review so teams can inspect UI changes as artifacts tied to specific builds. Its governance and API-driven automation work best when visual baselines and review lanes are treated as part of the release process.
- +CI-driven visual diffs with build-linked artifacts for faster triage
- +Branch-based review workflow for approving or rejecting UI changes
- +API automation for wiring Percy into custom pipelines
- +Baseline management reduces repeated noise across builds
- –Adopting stable visual baselines can take ongoing tuning
- –Large diffs across dynamic pages can increase review load
- –Advanced governance needs stronger team discipline for consistent usage
- –Parallel throughput depends on the runner and browser coverage setup
QA release managers
Visual diffs tied to each build
Fewer UI regressions shipped
Front-end QA engineers
Branch-based UI review lanes
Faster change review
Show 1 more scenario
Engineering test automation teams
API-driven visual baseline governance
Consistent visual standards
Teams automate baseline updates and visual checks through programmatic workflows.
Best for: Fits when QA teams need CI-integrated visual regression review for fast UI change approvals.
Postman
API-firstAPI platform that includes manual and automated API testing, monitoring, and collaboration.
Collection runs with embedded pre-request and test scripts provide executable assertions per request.
Postman collections let teams bundle request sets with pre-request scripts and test scripts, so the tool can validate response codes, headers, and payloads in one run. Environments and variable scopes reduce duplication when the same tests must run against dev, staging, and production-like targets. For execution automation, Postman can run collections from CI runners and export results, which supports continuous integration testing for APIs. For broader integration testing workflows, its HTTP-focused model pairs well with mocks and contract-first collaboration when services are still evolving.
A key tradeoff is that Postman is not designed to manage full UI test suites or end-to-end browser automation like Selenium-first toolchains. Teams typically use it for service-level regression tests and API contract checks, then hand off UI coverage to separate test automation frameworks. A common usage situation is validating a REST or GraphQL change by running the same collection across multiple environments and gating merges on pass or fail outcomes.
- +Collections package requests with test scripts for repeatable API regressions
- +Environment and variable scopes reduce duplication across dev and staging targets
- +CI collection runs produce structured results for gating and trend review
- +Mock servers and stubs support contract validation when services lag
- –Limited fit for browser-driven end-to-end testing compared to Selenium ecosystems
- –Complex test suites require disciplined scripting to avoid brittle assertions
- –Non-HTTP workflows need separate tooling since the request model is HTTP-centric
- –Large collections can slow navigation and increase maintenance overhead
API QA engineers
Automated endpoint regression with assertions
Fewer regressions escape merges
Backend teams integrating services
Mock-driven contract validation
Faster integration test cycles
Show 1 more scenario
Platform QA leads
Multi-environment test execution
Consistent coverage across environments
Swap environment variables so the same suite targets staging and production-like systems.
Best for: Fits when QA teams need API-focused regression suites with CI automation and reusable request artifacts.
Applitools
specialistVisual testing platform for UI validation, layout checks, and test stability improvement.
Visual AI-driven comparisons generate actionable UI diffs from rendered output, not DOM snapshots.
Visual regression coverage is the core capability, because Applitools compares rendered UI output and highlights mismatches across browsers and screen sizes. The workflow fits teams that already maintain Selenium-style end-to-end tests and want stability from pixel-level evidence rather than brittle locators. Automation support includes integrations that let tests submit results and retrieve visual diffs as part of CI runs.
A practical tradeoff is that visual baselines and viewport strategy require deliberate setup, because dynamic content often needs targeted handling to avoid noisy diffs. Applitools fits best for regression test suites where UI changes are frequent and manual UI review does not scale.
- +Visual regression compares rendered UI output across browsers and devices
- +CI-friendly reporting produces diffs and review artifacts for change triage
- +Baseline management supports repeatable UI verification across releases
- +Framework integrations reduce the need for custom visual comparison code
- –Baseline and viewport selection requires ongoing governance to reduce noise
- –High UI churn can raise review workload when diffs frequently change
- –Some pages need extra stabilization to handle dynamic regions
- –Teams may need additional orchestration effort for complex test environments
QA leads managing UI regression
Catch UI breaks across browsers
Faster UI change triage
Automation engineers on Selenium pipelines
Reduce flaky UI assertions
Fewer brittle locator failures
Show 2 more scenarios
Release managers coordinating signoff
Review visual changes per release
Clearer UI release decisions
Baselines and diffs provide a consistent artifact set for each deployed build.
Product teams with frequent UI updates
Validate responsive UI layouts
Less layout regression risk
Cross-device viewports help confirm layout stability for key screen sizes.
Best for: Fits when teams need reliable UI regression evidence with visual diffs in CI.
Mabl
SMBLow-code test automation platform focused on end-to-end web testing and regression coverage.
AI-assisted, change-tolerant test execution that reduces locator breakage during UI updates.
Mabl executes end-to-end tests using browser-based journeys that are authored as visual workflows and then run automatically on every configured schedule. Core capabilities include test orchestration, cross-browser execution, and built-in environment control for data inputs and URL targets.
- +Visual journey authoring reduces brittle locator churn in browser flows
- +API-managed test and environment lifecycle fits CI orchestration
- +Configurable execution across browsers for consistent regression coverage
- +Automatic test failure triage with execution artifacts and screenshots
- –Advanced custom logic still requires careful design around workflow boundaries
- –Team-wide governance can be heavy without enforced naming and ownership rules
- –Extensibility for niche test runners depends on integration points
- –High-volume parallel runs may require tuning to keep feedback loops tight
QA operations teams
Nightly runs on selected environments
Ongoing regression visibility
QA engineers and integration testers
UI journey plus API verification
Coverage of end-to-end journeys
Show 2 more scenarios
Web engineering and QA teams
Parallel browser verification for speed
Shorter feedback cycles
Teams partition suites to run concurrent browser checks for faster feedback across browsers.
QA, product, and acceptance teams
Reusable acceptance and system checks
Reusable full process validation
Teams automate managed user-facing scenarios that validate complete processes end to end.
Best for: Fits when QA teams need frequent end-to-end regression runs with low maintenance for browser UI flows.
TestRail
SMBTest case management software for organizing QA processes, runs, and reporting.
Requirement mapping with trace views connects executed runs back to product requirements.
TestRail supports planning and execution flows via test suites, test cases, test runs, and result publishing with granular statuses and attachments. Work item mapping is handled through requirement associations so teams can view coverage and identify gaps between requirements and executed tests. Custom fields and configurable statuses help align reporting to a specific QA process without changing the core model. Automation teams typically use the REST API to create plans, submit results, and keep CI builds synchronized with run outcomes.
- +REST API supports automation posting for test runs and results
- +Requirement to test mapping gives actionable trace coverage views
- +Configurable fields and statuses adapt to existing QA reporting
- +Evidence attachments and comments keep execution context attached
- –Execution and environment orchestration require external tooling
- –Automation setup depends on consistent project and run conventions
- –Complex cross-team governance can require extra process design
- –Advanced reporting depends on configuration and discipline
QA teams in CI pipelines
Automate result publishing from builds
Execution history stays current
Product compliance programs
Track requirement coverage through runs
Coverage gaps become visible
Show 1 more scenario
Engineering teams scaling manual testing
Standardize cases with configurable fields
Reports stay comparable
Custom fields and statuses enforce consistent reporting across suites and plans.
Best for: Fits when QA teams need centralized test case and execution tracking with API-driven updates.
Playwright
enterpriseMicrosoft-maintained open-source automation library supporting Chromium, Firefox, and WebKit with cross-language bindings.
Built-in tracing with step-by-step replay to diagnose flaky UI and async timing issues.
Playwright drives end-to-end browser tests by controlling Chromium, Firefox, and WebKit with a single automation API. It supports async-safe locators, network interception, and deterministic waits that reduce flaky UI checks.
- +Cross-browser engine support through one automation API
- +Network interception enables contract-adjacent UI checks
- +Trace viewer captures DOM, network, and screenshots for failures
- +Parallel execution works at the test-run level
- –Test code maintenance can grow with complex selector strategy
- –Scripting heavy tests requires stronger JavaScript or TypeScript practices
Developers validating web UIs
Browser-driven API response checks
Coordinated UI and API checks
Development and QA teams
Cross-browser regression before release
Earlier browser regression detection
Show 2 more scenarios
Developers and QA engineers
End-to-end workflow automation
Key workflows verified
Teams automate critical user journeys like sign-in and checkout using multi-step browser interactions.
QA teams and developers
Headed or headless usability reproduction
Repeatable interaction validation
Teams run scripted flows under specific headed sessions or viewport settings to validate interactions.
Best for: Fits when teams need cross-browser UI automation with strong debugging signals in CI.
Cypress
SMBJavaScript-native end-to-end testing framework with real browser execution, time travel debugging, and component testing support.
Real-time runner with built-in time-travel style debugging for browser actions and assertions.
Cypress is an end-to-end test automation framework built around real-time browser execution and interactive debugging. Tests run in a browser-like runtime with automatic waiting, network stubbing, and strong developer feedback via the test runner UI.
- +Interactive test runner shows step-by-step DOM state during failures
- +Automatic waiting and retry behavior reduces flaky timing assertions
- +Network stubbing and request interception support deterministic E2E tests
- +Execution model aligns browser actions with assertions in a single flow
- –Browser-first execution can feel less direct for API-only testing
- –Advanced cross-browser coverage depends on external setups and browsers
Front-end and QA teams
Repeatable UI checks after changes
Earlier UI break detection
Development and DevOps teams
Continuous integration release gates
Automated release feedback
Show 2 more scenarios
Web application test teams
Deterministic flows with stubs
Controlled test conditions
Teams stub network requests and control state so repeat runs behave consistently for key scenarios.
Web product and QA teams
Browser compatibility behavior checks
More browser regression visibility
Teams run web tests across configured browsers to identify rendering and behavior differences.
Best for: Fits when QA teams need fast, debuggable end-to-end regression tests with controllable network behavior.
Apache JMeter
enterpriseOpen-source load and performance testing tool for web applications, APIs, and databases with a GUI test plan designer.
Thread group scheduling plus built-in listeners provide end-to-end throughput and latency metrics for long-running workloads.
Apache JMeter is used by QA teams to run scripted performance and functional checks from the same test plan model, with strong support for HTTP-based systems via built-in samplers. It excels at load, stress, and soak testing with configurable thread groups, scheduling, and detailed response-time and assertion metrics.
- +Thread group controls support ramp-up, scheduling, and long-running soak tests
- +Built-in HTTP samplers with assertions and listeners produce detailed timing metrics
- +Extensible sampler and assertion framework supports custom protocols and logic
- +Headless execution supports CI regression runs with XML reports and JTL output
- –GUI test plan editing can slow down large, highly parameterized suites
- –Test data management often requires external CSV or custom scripts per scenario
- –Governance features like RBAC and audit logs are not built into the core tool
- –Functional coverage is limited outside HTTP-centric systems without added components
Database engineers and backend teams
Concurrent JDBC workload generation
Bottlenecks identified under load
Performance engineers and operations
Long-duration soak endurance runs
Stability degradation evidence
Show 2 more scenarios
Platform and capacity planning
Ramp traffic to saturation points
Throughput limits estimated
Teams increase concurrency with configurable ramping profiles to find throughput limits and failure behavior.
DevOps and delivery teams
CI load smoke plan thresholds
Performance regression feedback
Teams run lightweight load plans from command line or CI with response and error thresholds.
Best for: Fits when QA teams need repeatable load and API-level regression from maintainable test plans.
Selenium
enterpriseOpen-source browser automation framework for functional web testing across multiple browsers and languages.
Selenium Grid provides distributed WebDriver execution that coordinates nodes for parallel cross-browser runs.
Selenium is distinct in its browser automation engine that drives tests through multiple language bindings, with WebDriver as the core API surface. It runs end-to-end browser regression across cross-browser setups and supports parallel execution through Grid for distributed runs.
- +WebDriver API is widely adopted across Java, Python, C#, and JavaScript
- +Selenium Grid enables distributed browser runs for faster regression suites
- +Large community and plugin ecosystem for reporting and test utilities
- +Direct control over browser actions supports detailed UI verification
- –No built-in test case management or execution history UI
- –Stable suites require disciplined waits, selectors, and test isolation
- –Cross-browser reliability depends on environment and driver compatibility
- –Advanced flows often need custom framework code around core Selenium
QA and SDET teams
Reusable cross-browser test suites
Broader browser coverage
Software development and QA teams
End-to-end user journey automation
Functional flows validated
Show 2 more scenarios
QA and CI teams
Targeted UI smoke workflows
Fast critical-flow feedback
Teams run focused browser workflows after code changes or deployments for quick flow validation.
Enterprise test infrastructure teams
Grid-based concurrent browser sessions
Higher test throughput
Teams run tests in parallel on remote machines to raise throughput across browser and operating-system combinations.
Best for: Fits when teams need customizable browser automation and can build their own test governance.
ACCELQ
enterpriseCodeless automation platform for web, API, mobile, and packaged application testing.
Visual orchestration that connects UI steps and API validations into one regression test workflow with shared reusable assets.
ACCELQ targets QA teams that need no-code test automation while still supporting API-level testing and CI execution. It provides a visual workflow editor for building regression test suites and generating reusable test assets.
- +Visual test authoring reduces script work for end-to-end regression runs
- +API testing support fits teams that mix UI checks with contract-style validations
- +Reusable test assets help keep large regression suites consistent
- +Role-based access and audit trail support controlled collaboration
- –Advanced edge cases still require engineering support beyond visual steps
- –Automation scalability depends on test execution parallelization setup and tuning
SAP QA and business process teams
Reusable SAP screen interaction scenarios
Repeatable SAP flow validation
Enterprise QA and business testing teams
Codeless cross-application journey validation
Consistent business workflow coverage
Show 2 more scenarios
QA analysts and data-driven testers
Parameterized data-driven scenario batches
Broader coverage with reuse
Teams load structured fixtures and parameterize workflows for multiple test scenarios with less duplicated logic.
Release validation and enterprise QA ops
Multi-environment regression runs
Repeatable environment-based regression
Teams execute the same regression suite across configured environments and browser combinations.
Best for: Fits when QA teams want visual automation plus API testing inside a controlled, collaborative release workflow.
Conclusion
After evaluating 10 it testing software, Percy 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 It Testing Software
This buyer's guide narrows in on it testing software used by QA teams to plan, execute, and report across regression workflows. Coverage includes TestRail for requirement-to-execution traceability, and it expands through tools like Postman for API regression runs and Applitools for visual UI diffs. The selection also covers Percy for pull-request linked visual review, Mabl for change-tolerant end-to-end execution, and ACCELQ for shared visual and API validation workflows. Browser and debugging coverage comes from Selenium Grid, Cypress real-time tracing, and Playwright tracing, while Apache JMeter supports throughput-focused load and soak-style runs.
The buying criteria focus on where teams get control through integration depth, automation and API surface, and governance around runs, baselines, and execution conventions. TestRail emphasizes REST API posting into centralized test case and execution history, while Postman packages request-scoped tests and variables into reusable CI artifacts. Applitools and Percy shift UI regression evidence into CI review workflows using rendered visual diffs instead of DOM snapshots. Selenium Grid and Playwright prioritize distributed execution and debugging signals, while JMeter provides scheduled workload execution with built-in timing metrics.
It Testing Software for QA execution tracking, automation, and regression evidence
It testing software covers the full loop of creating tests, running them in repeatable environments, and collecting evidence that ties results to what was expected. TestRail targets centralized test case management plus execution history, with requirement mapping that connects executed runs back to product requirements.
It also includes automation engines that move assertions next to the actions that generate them. Postman supports collection runs with embedded test scripts and environment variables so API regression suites run consistently across dev and staging targets.
Execution governance, automation surface, and evidence traceability
QA teams need more than test execution because regression workflows break when evidence cannot be traced to what was expected. The most decisive feature set shows how runs link back to requirements or change requests, how automation is attached to artifacts, and how governance controls prevent review noise.
Traceability from requirements to executed runs
TestRail connects executed runs back to product requirements through requirement mapping and trace views. This creates actionable trace coverage views when teams centralize planning and execution history.
API-regression packaging with request-scoped assertions
Postman packages requests with embedded pre-request and test scripts inside collections. Collection runs with environment and variable scopes reduce duplicated test scripting across dev and staging targets.
CI-friendly visual evidence with review-oriented diffs
Applitools generates visual AI-driven comparisons that produce UI diffs from rendered output. Percy adds pull-request focused visual review with build-linked diffs that route approvals to UI change reviewers.
Distributed browser execution and debugging signals
Selenium Grid coordinates nodes for parallel cross-browser WebDriver execution when teams build custom governance. Cypress provides a real-time runner with time-travel style debugging, while Playwright includes built-in tracing with step-by-step replay.
Change-tolerant end-to-end execution for frequent UI updates
Mabl uses AI-assisted, change-tolerant test execution designed to reduce locator breakage during UI updates. ACCELQ adds visual orchestration that connects UI steps and API validations into one regression workflow with shared reusable assets.
Scheduled workload execution with built-in timing metrics
Apache JMeter uses thread group scheduling plus listeners to produce end-to-end throughput and latency metrics for long-running workloads. It supports ramp-up and scheduling for soak-style runs with HTTP samplers that include assertions.
Pick the workflow shape that matches evidence, automation, and control needs
The category splits into two practical philosophies. Some tools centralize test case management and execution history so governance lives in a single system, while others center on executable artifacts like request collections or visual baselines so automation lives with the evidence. Decision points should match where failures are debugged and where evidence is reviewed, because teams lose time when run context and artifacts are separated across tools.
Choose the system of record for test planning and results history
If QA teams need centralized test case management with execution history UI, TestRail fits through requirement mapping and trace views. If teams prefer evidence and automation artifacts to be the primary unit, Postman collections and Applitools or Percy visual diffs shift the center of gravity.
Match the automation surface to how tests are authored and maintained
For API regression suites that reuse request artifacts, Postman collection runs with embedded test scripts support executable assertions per request. For browser automation with maintainable debugging signals, Cypress and Playwright focus on runner and tracing experiences rather than test management screens.
Decide where UI regression evidence is reviewed during change cycles
If approvals need to land on pull requests with build-linked artifacts, Percy supports branch-based visual review workflows. If teams want visual AI-driven comparisons that reduce reliance on DOM-level snapshots, Applitools produces rendered-output UI diffs for CI triage.
Select the execution model based on parallelism and control expectations
When distributed execution is required and teams accept building governance around WebDriver, Selenium Grid enables parallel cross-browser runs. When cross-browser execution is needed without separate grid coordination, Playwright provides a single automation API plus tracing to diagnose flaky UI and async timing issues.
Use change tolerance or workload scheduling only when the workflow demands it
If UI updates change selectors frequently and regressions must stay low-maintenance, Mabl is built for change-tolerant execution with AI-assisted behavior. If regressions include repeatable throughput and latency validation with scheduled ramp-up and soak-style workloads, Apache JMeter uses thread group scheduling and listeners with detailed timing metrics.
Which QA teams fit each evidence and governance model
Teams should match their release workflow to the tool that owns evidence, not just the tool that runs tests. The right fit depends on whether traceability, API artifact reuse, visual review loops, or distributed browser execution is the dominant operational need.
QA teams standardizing requirement-to-execution traceability
TestRail supports requirement mapping and trace views that connect executed runs back to product requirements. This helps teams manage regression coverage without relying on manual cross-referencing.
QA teams building API regression suites with reusable request artifacts
Postman collection runs include embedded pre-request and test scripts and reuse environment variables. This supports CI execution where request scope and assertions stay attached to the artifact.
QA teams running visual UI regressions with code review workflows
Percy links visual diffs to pull requests and build artifacts to route approvals to UI change reviewers. Applitools complements this with AI-driven visual comparisons from rendered output for CI-based triage.
QA teams scaling browser regression execution and debugging
Selenium Grid supports distributed WebDriver execution for parallel cross-browser runs. Cypress and Playwright add debugging signals through real-time runner inspection or built-in tracing replay in CI.
QA teams mixing UI flows with API validations inside a single release workflow
ACCELQ connects visual test steps with API testing inside one regression workflow using shared reusable assets. Mabl targets frequent end-to-end regression runs where UI changes should not destroy locator strategy.
Common failure modes when adopting IT Testing Software for QA
Adoption failures usually come from mismatched ownership of evidence and runs rather than missing features. Teams also waste time when visual baselines or execution conventions are handled without governance, which turns regression feedback into review noise.
Treating a test execution engine as a full execution-history system
Selenium Grid and Cypress focus on execution and debugging, and Selenium does not provide built-in test case management or execution history UI. If centralized tracking and trace views are required, TestRail is the execution history anchor.
Building brittle visual checks that create high diff churn
Applitools requires baseline and viewport governance, and high UI churn can raise review workload when diffs frequently change. Percy also needs ongoing tuning for stable visual baselines when dynamic pages generate large diffs.
Mixing API testing needs into browser-first automation without a clean boundary
Postman is limited for browser-driven end-to-end testing compared to Selenium ecosystems, while Mabl and Cypress are browser-first tools. Teams should keep API regression suites in Postman and use browser tools for true UI flows.
Running load and soak validation as if it were a functional regression suite
Apache JMeter thread group editing can slow down large, highly parameterized suites, and test data management often needs external CSV or custom scripts. Teams should plan for workload scenario design rather than expecting JMeter to replace functional test management.
Underestimating governance costs for shared naming and ownership in change-tolerant execution
Mabl can reduce locator breakage, but team-wide governance can be heavy without enforced naming and ownership rules. ACCELQ also depends on execution parallelization setup and tuning for automation scalability.
How We Selected and Ranked These Tools
We evaluated TestRail, Postman, Applitools, Percy, Mabl, ACCELQ, Selenium, Cypress, Playwright, and Apache JMeter against integration depth, automation and API surface, and governance controls for runs and evidence. Features accounted for 40 percent of the scoring, and ease and value each accounted for 30 percent.
TestRail earned the top position because requirement mapping with trace views connects executed runs back to product requirements and pairs that traceability with a REST API for automation posting. The ranking also favored tools that keep evidence artifacts linked to execution context, like Postman collection runs and Applitools or Percy CI diff workflows.
Frequently Asked Questions About It Testing Software
How do TestRail and Xray differ in how teams structure test cases and trace results to requirements?
What API-based integration patterns work for posting automation results into TestRail?
Which tool handles SSO and RBAC for test execution governance better, ACCELQ or TestRail?
How do Postman and Playwright differ for API contract testing and end-to-end coverage in CI?
What breaks if a team relies on Selenium WebDriver alone for test-case management and reporting?
When does Applitools outperform DOM-based assertions for cross-browser UI regression?
How do Percy and Applitools handle visual diffs during pull-request review workflows?
How does Mabl manage test data and environment configuration for end-to-end regression suites?
What is the tradeoff between Cypress and Playwright for debugging and network control during flaky UI investigations?
Where does JMeter fall short compared with functional regression tools like Selenium for end-to-end testing?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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