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IBM Rational Functional Tester (RFT): Core Concepts, Workflow, and Integrations

IBM Rational Functional Tester (RFT) is a functional UI automation tool used to record, edit, and replay tests against web and desktop applications. If you are evaluating legacy automation stacks or maintaining an existing RFT suite, this guide summarizes how RFT works, what it supports, and how to organize stable test assets.

Originally published on 2010-02-25; fully revised and updated.

rational-functional-testing-intro

What RFT is designed to do

RFT is built for full functional testing through record-and-playback and script-based automation. During test creation, it can capture UI interactions and generate scripts that validate object state through verification points.

  • Record and replay end-to-end user flows.
  • Create object-oriented test scripts in Java or VB.NET environments.
  • Insert verification points to compare expected and actual object/data states.
  • Maintain recognition properties in a centralized object map.

IDE and scripting options

The original RFT model provides two scripting tracks tied to two IDE ecosystems.

RFT optionLanguageDevelopment environmentTypical fit
Functional Tester, Java ScriptingJavaIBM Rational Software Delivery PlatformTeams standardizing on Java-based tooling
Functional Tester, VB.NET ScriptingVB.NETMicrosoft Visual Studio .NETTeams aligned with Microsoft .NET development workflows

How object-based testing works in RFT

RFT uses object properties for identification instead of raw screen coordinates. This makes playback more resilient when UI layout changes but underlying object identity remains stable. The source material also states that testing can target visible and non-visible objects across Java, VB.NET, Windows, and web applications.

Test object maps as a shared recognition layer

When recording, RFT automatically creates a test object map. This map stores recognition properties and allows centralized maintenance: if an object definition changes, scripts using that map can inherit the update without rewriting each script.

Verification points and baseline comparison

Verification points capture expected state into baseline data, then compare future executions against that baseline. The original post lists one object-properties verification point and five data verification point types.

  • Object properties
  • Menu hierarchy
  • Table
  • Text
  • Tree hierarchy
  • List

Differences can then be analyzed with the Verification Point Comparator, and baseline data can be updated when expected behavior changes.

Typical RFT test lifecycle

flowchart TD
    A[Choose IDE and language] --> B[Record user workflow]
    B --> C[Auto-create object map]
    C --> D[Add verification points]
    D --> E[Run playback on target environment]
    E --> F{Matches baseline}
    F -- Yes --> G[Mark pass and store log]
    F -- No --> H[Review comparator differences]
    H --> I[Update baseline or open defect]

Platform and integration model

The source describes platform-independent and browser-independent playback, and historical browser examples including Firefox, Mozilla, Internet Explorer, and Netscape. It also describes integration with Rational TestManager and Rational ClearQuest Test Manager when those components are installed.

ComponentWhat it addsPrerequisite noted in source
Rational Functional TesterRecord/playback scripting, object maps, verification pointsRFT installation
Rational TestManager integrationRun scripts from TestManager and use TestManager logging featuresTestManager installed
Rational ClearQuest Test Manager integrationPlayback from ClearQuest TestManager, generate logs, track defectsClearQuest Test Manager installed

Example repository layout for RFT assets

For maintainability, keep scripts, object maps, and baseline artifacts versioned together.

rft-tests/
  scripts/
    smoke/
    regression/
  object-maps/
  verification-baselines/
  playback-logs/
  docs/

Best practices for stable RFT suites

  • Keep object maps modular so UI changes are isolated.
  • Review baseline updates carefully to avoid accepting real regressions.
  • Separate smoke and regression suites to speed feedback.
  • Store logs and comparator outputs with build metadata for traceability.
  • Validate current platform and browser support against up-to-date vendor documentation before scaling execution.

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I'm Rajesh Kumar, a DevOps, SRE, DevSecOps, Cloud, and Platform Engineering expert passionate about sharing practical knowledge, real-world experiences, and industry best practices. I have worked at Cotocus and regularly write about technology, travel, investing, health, product reviews, and digital marketing through my various platforms. I publish technical articles at DevOps School, travel stories at Holiday Landmark, stock market insights at Stocks Mantra, health and fitness guidance at My Medic Plus, product reviews at TrueReviewNow, and SEO and digital marketing strategies at Wizbrand.

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