Test automation: Benefits & strategies for S/4HANA success

Pain points

Test automation: From challenge to high potential

Test automation has become a cornerstone of modern software development, ensuring quality is built into every stage of the process, from initial design to final deployment. Despite the advancements in software testing tools and methodologies, however, surveys like the GitLab report reveal that 80% of software testing is still done manually. Manual testing, while essential in specific scenarios, can be slow, error-prone, and costly when scaled across complex systems. Test automation addresses these challenges by offering speed, precision, and scalability. This blog delves into the benefits of test automation and outlines a strategic approach to its successful implementation.

Test automation significantly reduces the time required for repetitive testing cycles. Tasks that would take days manually can now be completed in hours or minutes.
Tools like Tosca enable seamless end-to-end automation, particularly valuable in hybrid system landscapes that are integral to modern enterprise environments.

Although automation requires an initial investment, it reduces long-term costs by eliminating repetitive manual testing during iterative development phases.
Automated regression testing ensures consistency across software versions without additional overhead.

Automation minimises the risk of human error, ensuring tests are executed precisely.
Logs and error reports generated by tools provide deeper insights into system behaviour, making it easier to identify and address issues.

Automation allows testing of a broader range of scenarios and combinations, including integration tests often overlooked during manual testing.
The modular approach enables the reuse of pre-built components, increasing coverage without proportional effort.

Implementing automation early in the project lifecycle facilitates faster defect detection and resolution, improving software quality from the outset.

Automation frameworks are inherently scalable, allowing organisations to expand their testing capabilities as projects grow.

1. Analyse pain points

Assess inefficiencies in manual testing to identify areas where automation can have the most significant impact.

2. Select appropriate tools

Invest in tools that align with project requirements. Tools like Tosca, known for their versatility and robust feature set, are ideal for complex environments.

3. Develop a test automation strategy

Define objectives, success metrics, and use cases to provide clarity and direction.

4. Build an automation framework

Focus on modularity and scalability, ensuring frameworks adapt to future needs.

5. Ensure robust test data management

Centralise and automate data management to ensure consistency across tests.

6. Integrate with development processes

Establish strong communication channels between development and testing teams to address changes promptly.

7. Validate and maintain test cases

Regularly update test cases to reflect system changes and perform periodic impact analyses.

Capabilities

The must-have features for test automation tools

Choosing the right test automation tool is pivotal to the success of your testing strategy. A tool's capabilities can make or break its effectiveness, especially in complex, multi-system landscapes. Below are the essential features every robust test automation tool should offer.

1. Comprehensive user interface (UI) automation

A test automation tool must support automation across all UI types, whether it’s HTML, desktop applications, or hybrid interfaces.
Without this capability, achieving full automation in multi-system environments becomes nearly impossible. UI automation ensures that diverse user interactions are covered, regardless of the platform or technology stack.

2. Robust API testing

The ability to test APIs is critical for validating back-end processes and system integrations.
A powerful tool integrates seamlessly with APIs, enabling end-to-end testing by bridging UI interactions with underlying data flows. This feature ensures that all layers of your application work harmoniously.

3. Model-based and risk-based testing

Model-based testing: Tools should allow the creation of reusable modules for testing. For instance, once a module for generating purchase orders in SAP is built, it can be adapted for different end-to-end scenarios. Re-using modules reduces effort and accelerates testing.
Risk-based testing: Tools should prioritise tests based on the criticality and complexity of business processes, focusing resources where they add the most value.

4. Integrated test data management

Effective test data management is non-negotiable. Without accurate and accessible test data, even manual tests struggle to deliver reliable results, let alone automated ones.
The tool should:
- Enable dynamic management of test data using variables.
- Support seamless sharing of test data across multiple systems for integrated testing.
While integrated test data itself isn’t managed directly by the automation tool, the tool must facilitate data inclusion for streamlined automation.

5. Service virtualisation

Service virtualisation is an advanced feature allowing testers to simulate components that are difficult to access, such as mainframe systems without staging environments.
This capability empowers testers to conduct end-to-end testing even when certain services or systems are unavailable.
Though not commonly used, service virtualisation can be a game-changer in environments with significant constraints.

To sum it up, a great test automation tool does more than just execute scripts. It bridges gaps in complex systems, ensures data accuracy, and offers advanced capabilities like service virtualisation for seamless integration testing. By prioritising these must-have features, organisations can set up a scalable and efficient testing framework.

Challenges in SAP S/4HANA Transformations

Complex Landscapes

S/4HANA implementations often involve integrating multiple systems, making end-to-end testing essential but challenging when done manually.

Automation tools like Tosca ensure comprehensive testing of all system interfaces, from web applications to backend ERP functionalities.

Frequent updates

SAP's quarterly updates demand rapid regression testing to ensure system compatibility without interrupting operations.
Automated testing accelerates the validation process, maintaining the stability of core processes.

Dynamic data management

Consistent test data across multiple systems is critical. Automation ensures data accuracy and prevents discrepancies during integration tests

Key transformation paths

Greenfield approach

Starting from scratch offers the advantage of integrating automation early. Pre-built best practices, such as automated test cases for standard SAP processes, can be adapted to project-specific requirements.
Example: Automated scripts for order creation enable teams to validate core functionalities without delay.

Brownfield approach

Brownfield projects involve migrating existing systems to S/4HANA, which poses challenges like legacy system dependencies and inconsistent test strategies.
Automation can bridge gaps by standardising processes and enabling selective testing of affected components.

By tailoring automation strategies to these transformation paths, organisations can ensure seamless transitions while optimising testing efficiency.

For further information on transformation paths, please visit our blog on Brownfield, Greenfield, and Colorfield.

High impact

Overcoming challenges in test automation

While test automation offers significant benefits, it is not without challenges. Proactively addressing these ensures a sustainable, high-impact testing strategy.

Artificial Intelligence

How AI helps advance test automation

In test automation, artificial intelligence is becoming increasingly important. With its ability to analyse vast amounts of data, AI can automatically generate test cases tailored to specific application scenarios and analyse the results.

Tools like Tosca leverage embedded AI features, such as a Copilot, to take the testing process to entirely new levels. AI-powered assistants can explain comprehensive test cases. They also streamline result analysis by detecting patterns, anomalies, and potential defects with greater accuracy. Beyond these capabilities, AI enhances efficiency in test maintenance, optimisation, and even supports continuous testing in agile environments, making the overall testing process faster, smarter, and more reliable.

Benefits of AI

To sum it up, AI brings a whole new quality to test automation. Its key benefits include

Scalability: AI can quickly produce large datasets for extensive testing.
Edge Case Coverage: AI can identify and test edge cases that manual methods might overlook.
Realism: AI-generated data can mimic real-world usage patterns, improving test relevance.
Efficiency: Automation engineers can focus on designing robust test scenarios rather than manually preparing data.
Accuracy: As AI excels in identifying patterns and anomalies faster, this not only accelerates feedback loops, it also significantly enhances the accuracy of tests and subsequently the quality of the software tested