As the premier customer interaction platform in the industry, Microsoft Dynamics CRM revolutionizes sales, marketing, and service operations across the globe.
However, doing comprehensive testing on this vast and versatile platform creates considerable obstacles.
With frequent updates and customizations, the range of possible use cases and scenarios is massive for Dynamics CRM.
Defining and maintaining test cases manually requires enormous effort resulting in low test coverage. This is where generative testing comes in.
Unlike scripted tests, generative testing leverages the power of machine learning to automatically generate and execute thousands of unique, relevant test cases.
Let us explore generative testing techniques to significantly amplify real-world test coverage for Dynamics CRM deployments.
Model-Based Test Case Generation
The foundation of effective generative testing involves building a behavior model of the application under test – in this case, Microsoft Dynamics CRM Services.
Sophisticated crawler bots are used to traverse various screens and workflows of the CRM system.
As the bots interact with the app, they construct a detailed model encoding information on various UI elements, input fields, API calls, database operations and more.
Complex machine learning algorithms continuously refine this model to capture intricate dynamics between CRM components.
Once the model reaches sufficient coverage, it is used as a test case blueprint.
Combined math is applied to create thousands of permutations and scenarios involving different users, data and workflows.
These generated test cases offer far wider coverage than any manual test plan. Model-based tools like Leapwork and Eggplant enable such AI-powered testing for Dynamics CRM.
Data-Driven Test Case Generation
Generating realistic test data is key to uncovering tricky edge case bugs in Dynamics CRM.
Static test data has limited utility for effectively exercising a complex platform like CRM across various situations. This is where the data-driven approach comes in.
By ingesting samples of real customer data, test generators can analyze field distributions, properties and relational dependencies.
Advanced deep learning models use this understanding to produce completely synthetic yet authentic-looking CRM test data.
Generating diverse datasets allows for hitting boundary conditions during testing. Personalized test data tailored to company workflows also uncovers breaks.
Using actual production data while scrubbing sensitive information provides even wider test coverage.
API-Driven Test Case Generation
While the UI layer is important, much of the business logic in Dynamics CRM resides in the extensive set of background APIs and integrations.
Testing these interfaces requires automated test case generation capabilities.
Modern test generators employ a range of techniques to target Dynamics CRM APIs.
Certain tools dynamically analyze API schemas and error codes to construct test inputs and expected outcomes.
Others observe API traffic from user tests to infer integration use cases.
Some even leverage natural language processing to translate UI flows into corresponding API calls and test steps.
Such API-aware testing uncovers bugs that pure UI testing would miss in CRM. It also accelerates test creation for custom-coded extensions and complex integrations.
Rapid Test Case Amplification
The true power of generative testing lies in the ability to create thousands of test cases instantly with minimal additional effort.
- For Dynamics CRM, even high-level model specifications allow tools to generate expansive test suites using mathematical amplification.
- Test generators randomly combine various CRM building blocks like workflows, data sets, user actions and customizations mathematically to produce a large number of test cases automatically.
- Smart sampling techniques ensure combinations maximize coverage while minimizing redundancy. Generated tests are then auto-executed on Dynamics CRM for bug finding.
- Such amplification allows easily scaling CRM tests across modules, usage scenarios and data varieties.
- Even small test bases expand into extensive test automation suites for comprehensive protection.
- Vendors like Functioned and Qualities leverage intelligent amplification for Dynamics CRM testing.
Continuous Test Case Updating
Dynamics CRM upgrades introduce new features, configuration options and extensions over time.
This changing landscape means test cases require constant updates to avoid gaps.
Manual test maintenance becomes impractical very soon. This is an area where AI-based testing shines.
Generative testing solutions utilize advanced change detection algorithms to track updates to Dynamics CRM instances.
New or updated components are automatically flagged for test case enrichment to retain coverage. Older redundant test scenarios are also removed using ML techniques.
Such constant updating of generated test suites ensures they keep pace with changing CRM environments.
Minimal manual intervention is needed to sustain test effectiveness over time. Various tools provide automated maintenance capabilities for Dynamics CRM test cases.
Optimizing Test Case Execution
Blindly running thousands of generated test cases on Dynamics CRM can be extremely time-consuming with redundant tests.
Optimizing execution to maximize bug finding while minimizing testing time is key. This requires smart test case scheduling algorithms.
Generative testing tools analyze historical failure data, test case similarity metrics and CRM usage statistics to categorize and prioritize generated tests.
Critical business scenario tests are executed first followed by negative, boundary and performance test cases.
Test sequencing and environments are optimized dynamically using real-time signals.
Such optimization minimizes average bug detection time while improving test ROI.
Machine learning transparently focuses test execution on high-risk and high-coverage areas for Dynamics CRM.
Solutions from Parasoft, and Functionize provide such AI-enabled test optimization.
User Journey Analysis
Dynamics CRM caters to a variety of real-world user workflows ranging from sales lead management to marketing campaign execution and customer service.
Testing the end-to-end business process flows across CRM is vital.
Generative testing solutions employ advanced analytics on production usage data to construct frequently traversed user journeys automatically.
CRM telemetry covering form views, data access and custom API calls is aggregated to build journey maps.
Test generators transform these insights into journey-based test scenarios for comprehensive validation.
Business Process Testing
Business process testing is essential for maintaining the integrity and efficiency of CRM systems.
These systems are often deeply integrated into various business workflows, making it crucial to test the connected processes thoroughly.
To facilitate this, test generation tools play a vital role by analyzing log data from live Dynamics CRM instances.
These tools can identify the key business processes that rely on CRM data, allowing testers to focus their efforts efficiently.
Additionally, custom connectors and API call patterns are instrumental in mapping out complex, multi-step processes that involve interactions between different systems.
Once these processes are identified and mapped, test creators can transform them into integration test cases.
These test cases help ensure that changes or updates to the CRM system do not disrupt the continuity of these critical business processes.
By conducting thorough business process testing, organizations can minimize the risk of system failures, ensure a smooth user experience, and maintain the overall integrity of their CRM systems.
Negative Testing & Exploratory Testing
While testing happy paths is important, subjecting Dynamics CRM to incorrect or unexpected inputs reveals hard-to-catch issues.
- Generative testing allows easy incorporation of negative test cases.
- Test generators automatically combine valid use cases with techniques like data fuzzing, workflow disruption and fault injection to generate hundreds of break-it tests.
- Executing these resiliently uncovers potential failure points across CRM scenarios – improving overall robustness.
- Generative testing offers wide coverage but human testers bring intuitive context for tricky edge cases.
- Combining both allows leveraging AI to guide manual exploratory testing for Dynamics CRM.
- Test tools track manual tester actions on CRM and analyze logs to construct behavior models on the fly.
- These models then highlight unused functionality and high-value test areas to maximize exploration value.
Guiding manual testing allows combining wide coverage with real-world context.
A/B Testing
While improving test coverage, generative testing also allows easy experimentation to discover configuration issues.
Multiple Dynamics CRM instances with varying settings can be tested in parallel.
The test generator automatically creates focused test cases to compare behaviors across CRM variants.
This enables using production telemetry to establish optimal configurations and customizations for CRM rollouts. Optimization through easy experimentation ensures maximum reliability.
Applying generative testing powered by AI and ML can significantly boost real-world test coverage for complex platforms like Dynamics CRM.
Automated test case generation frees testers from manual maintenance allowing deeper focus on innovation.
Does your organization face challenges in end-to-end testing for Dynamics CRM? How are you leveraging test case generation capabilities?
What new possibilities can AI open up to improve CRM quality? Share your thoughts below!
